Hailing the Ferry, oil on canvas painting by Daniel Ridgway Knight, 1888, Pennsylvania Academy of the Fine Arts.

Medieval manuscripts resisted obsolescence. Made by highly specialised craftspeople (scribes, illuminators, book binders) with labour-intensive processes using exclusive and sometimes exotic materials (parchment made from dozens or hundreds of skins, inks and paints made from prized minerals, animals and plants), books were expensive and built to last. They usually outlived their owners. Rather than discard them when they were superseded, book owners found ways to update, amend and upcycle books or book parts.

These activities accelerated in the fifteenth century. Most manuscripts made before 1390 were bespoke and made for a particular client, but those made after 1390 (especially books of hours) were increasingly made for an open market, in which the producer was not in direct contact with the buyer. Increased efficiency led to more generic products, which owners were motivated to personalise. It also led to more blank parchment in the book, for example, the backs of inserted miniatures and the blanks ends of textual components. Book buyers of the late fourteenth and throughout the fifteenth century still held onto the old connotations of manuscripts—that they were custom-made luxury items—even when the production had become impersonal.

Owners consequently purchased books made for an open market and then personalised them, filling in the blank spaces, and even adding more components later. This would give them an affordable product, but one that still smacked of luxury and met their individual needs. They kept older books in circulation by amending them, attached items to generic books to make them more relevant and valuable, and added new prayers with escalating indulgences as the culture of salvation shifted.

From: M Rudy, Kathryn. Piety in pieces: how medieval readers customized their manuscripts. Open Book Publishers, 2016.

Image: Parchment diptych with the measurements of Christ’s length and side wound, inserted into a French book of hours. Paisley, Renfrew District Museum and Art Gallery, Ms. 1, fol. 13–14 forming a diptych. Photo © Author, CC BY 4.0.

• If you can read this, you are not a machine. [Gittit Szwarc]
• Clay PCB. [Patrícia J. Reis] “Our Clay PCB is not made of plastic but instead clay collected from the forest in Austria that was carefully prepared and modeled in a shape of a tile with an imprinted circuit, and later fired with wood in the nature. Our conductive tracks use urban-mined silver and all components are re-used from old electronic devices.” Via Roel Roscam Abbing.
• The revenge of the home page. [New Yorker] “As social networks become less reliable distributors of the news, consumers of digital journalism are seeking out an older form of online real estate.” Via Roel Roscam Abbing.
• The ‘boring phone’: stressed-out gen Z ditch smartphones for dumbphones. [The Guardian] “The problem with offlining is that the world is increasingly difficult for people without a smartphone.”
• This Irresistible Revolution. [The Point Magazine] “One of the ways that I “get offline” in the morning is by running—after which I upload my run to Strava.” Via Arts&Letters Daily.
• Ben Grosser, artist and creator of Minus, the opposite of Facebook. [Techtonic podcast]
• 2024 Small File Media Festival: Call for Work. Deadline: 15 June.

The Ironwood bicycle is a wooden framed bike that people could make themselves with commonly available materials. It has a laminated wooden frame and fork to which standard bicycle components are bolted. The frame can be made with basic carpentry and metalwork skills, without the need for welding or soldering. The design is intended for small scale bicycle production that would support local employment as an alternative to importing bicycles from around the world. More: http://www.ironwoodbicycle.com.

Next week 21 May LTM does a talk in Brussels, reflecting on the work of architect Louise Morin in gallery “et al.” It is the last day you can see her exhibition.

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Darktable 3:RGB or Lab? Which Modules? Help!

Original post in French by Aurélien PIERRE, edited by the pixls community.

Darktable is slowly converging to a scene-referred RGB workflow. Why is that? What does it involve? How does the use of darktable change? Answers here…

This article begins with a 3 section introduction of the Lab space. You don’t need to understand it in detail in order to understand what happens next.

What is Lab?

The color space CIE Lab was published in 1976 by the International Commission on Illumination (CIE), in an attempt to mathematically describe the color perception of the average human being. Lab space aims to decouple the brightness information (L channel) from the chroma information (channels a and b) and takes into account the non-linear corrections that the human brain makes to the linear signal it receives from the retina. Lab space is derived from CIE XYZ space, which represents the physiological response of 3 of the 4 types of photo-sensitive cells in the retina (the cones).

The XYZ space represents what happens in the retina, and Lab represents what subsequently happens in the brain, but both color spaces are models, that is, attempts to describe reality and not the reality itself. There are always discrepancies between a model and reality, but these models are refined and improved as research progresses. Moreover, a model often represents reality only under certain conditions and assumptions, which define the area of validity of each model.

Regarding their respective areas of validity, XYZ works well almost all the time, Lab only works as long as the image has a contrast less than 100:1 (i.e. a maximum dynamic range of 6.5 EV). In the context of the creation of the Lab model in 1976, researchers were working with scanned negatives, and color negatives have a dynamic range of 6 to 7 EV. 6.5 EV is also the static contrast of the retina, and it was a little after 1976 that we realized that the brain was constantly performing HDR fusion of several images per second, meaning that static contrast as a model parameter doesn’t make much sense in the context of human vision.

What is CIE Lab for? It is intended to predict the perceptual difference between 2 colors (the delta E) and to make gamut adaptations when converting an image from one color space to another. One can then try to remap the gamut to the closest color in the target color space via strategies that minimize the delta E digitally.

The big disadvantages of Lab are:

1. It doesn’t work well for strong contrast (> 7 EV), and especially outside the range [1:100] Cd/m²,
2. It is not linear in hue, i.e. if one fixes a pixel’s a and b chromaticity components and changes only its brightness L, the same hue would be expected at a different brightness (this was the design purpose of the Lab space), however there is a slight shift in the hue, more or less marked depending on the original color of the pixel.

What is Lab doing in darktable?

The original idea was to allow separate manipulation of the brightness and chromaticity. In 2009, the year of the project’s creation, cameras had dynamic ranges quite close to Lab’s valid range; the idea was far from bad at the time, especially because darktable did not have a complex masking option then.

Advantages:

1. Lab, being a reference space and therefore independent of the display color, makes presets very easy to set up and transfer,
2. Lab sets the middle gray (18%) to 50%, so the interface is more intuitive (the middle gray is in the middle of the graph of the tones, for example).

Problems:

1. Today’s cameras have dynamic ranges that are largely outside of the conditions under which Lab is valid, which makes the defects of this space more apparent. With dynamic ranges from 10 to 14 EV at 100 ISO, any recent camera does HDR by default, and Lab is not designed to handle that much dynamic range
2. Pushing pixels in Lab space is very risky, especially when tackling compositing and image fusion with softened and feathered masks. We’ll get back to that, but it has to do with the next problem…
3. Lab is not adapted to physically realistic corrections, such as blurring, deblurring, denoising, and any filter that simulates or corrects for an optical effect.

In brief, Lab was a youthful mistake. That said, all other photo processing pieces of software seem to work by default in non-linear RGB spaces (with a “gamma” applied at the beginning of the pipe) that are basically equivalent (regarding their flaws and drawbacks for image filters).

How does Lab work?

Everything (e.g. the camera sensor) starts from a linear RGB space. We convert linear RGB to XYZ. For the purposes of the demonstration, we can consider the XYZ space as a special RGB space whose primary colors have been slightly manipulated (that’s not the case, but it behaves the same way). XYZ is also a linear space.

We then switch from XYZ to Lab by applying a “gamma correction” on the luminance channel (from Y to L), and a rotation on the channels a and b. Mathematically, Lab is like applying 2.44 gamma to linear RGB – it poses the same practical problem: it’s highly non-linear.

Summary

Lab doesn’t work for high-contrast images and doesn’t work well for images with moderate contrast. It encodes pixel values in a perceptual manner rather than physical one, which will pose a problem in the following. Lab was not designed for image processing, but only as a way to study human vision.

Precision: I have used the term “gamma” or “gamma correction” incorrectly here. Strictly, a gamma function is the specific (technical) electrico-optical transfer function (EOTF) of old-school CRT screens, which is a power function with an exponent between 1.8 and 2.2. Nowadays, people incorrectly name “gamma” any power function used for technical integers encoding or artistic lightness adjustments, which is confusing. Any encoding transfer function (using a power function or not) should be called OETF (Opto Electrical Transfer Function), and is used only to alleviate the limits of 8 bits integer file formats. Any artistic power-like brightness corrections should be called a tone curve. Even if the operation is the same, it does not have the same meaning and should not be applied at the same place in the graphics pipe. But ICC nomenclature continues to call “gamma” the exponent used to encode/decode RGB pixels when using integer file formats, so here we are, mixing unrelated concepts under an umbrella name just because the maths write the same. But, when communicating with people out of the industry, it’s often easier to use the incorrect name so that everyone sort-of understands, even if it carries on the confusion.
By the way, power-like OETF are completely unnecessary as long as you use floating point arithmetic and files format (32 bits TIFF, PFM, OpenEXR…).

The limits of non-linear spaces in image processing

First of all, what do we mean by “linear”? If y is linear with respect to x, it means there’s a relationship between x and y in the form y= a . x + b, where a and b are real constants. Linear means proportional to something plus or minus a constant.

So, when we talk about linear RGB space, we mean that the RGB values are proportional to something. But proportional to what?

The sensor counts the number of photons it receives at each photosite. Every pixel contains information on the light spectrum captured at its position, in the form of 3 intensities (red, green, blue). The coefficient of proportionality a between the number of photons and the final RGB value is the ISO sensitivity of the sensor. The constant b is the sensor noise threshold. The RGB signal is proportional to the energy of the light emission picked up by the camera sensor.

From the point of view of human perception, these intensities being proportional to the physical energy level of the light emission, does not make sense. In fact, the brain applies a non-linear, logarithmic correction that the Lab color space approximates using a cubic root. This means that we have an increased sensitivity to dim light, and reduced sensitivity to bright light.

However, all optical operations that are performed during image capture (e.g. lens blur, noise creation, or the effect of a color filter added to the lens) are applied directly to the photons. To reverse the lens blur or to simulate it when processing, we need to work on the linear RGB information, which is the closest thing to the photon data that is available to us.

See for yourself: Which one of these two computer generated bokeh (original below) seems the most natural to you? (See also a more spectacular example on Chris Brejon’s website)

Observe in particular how the dark silhouettes (bottom left) merge into the light background, or the contrast of the pentagons formed by the lens diaphragm on spotlights.

Another example, with a simple blur on smooth surfaces: Which of these gradations seems to you to be the most progressive?

These two examples were generated with Krita, which allows you to work in both linear and non-linear RGB, and has filter layers including a physically realistic lens blur.

This type of problem will occur the same way in darktable, as soon as you use the modules sharpen, high-pass, low-pass, and feathering/smoothing of drawn and/or parametric masks (which are blurs).

Blurring, deblurring, or anything else connected to optics must take place in linear RGB. There’s no mathematical model* that allows correct gradients in RGB encoded for display (with an OETF) or in Lab, due to loss of connection between pixel values and light energy.

* and just because the problems aren’t visible all the time doesn’t mean the problems aren’t always there. We can, up to a certain point, hide them with mathematical trickery (thresholds, opacity, etc.), but they will always end up coming out at the worst time. Trust me, I know exactly where to push to make it break.

This is also the problem that arises with hue zones blending in the color zones module (even if a tweak, introduced under the “smooth” process mode, attempts to hide this under the rug), which produces granular and sharp transitions.

The only darktable module that works in Lab to make a blur, and where it still works reasonably, is the local laplacian mode of the local contrast module. The price we pay for it to work is that it’s very computationally heavy and theory is like rocket science. And, even if the blur is stable, it comes with an ungracious desaturation with a hue shift to muddy grey-blue when you push the sliders a little too hard.

The benefits of a linear RGB treatment

So here’s where you say “as long as I’m not blurring my images or working only on color, I can still use Lab”.

That’s partly true, but in fact, even in those cases, working in linear RGB is simpler, with faster algorithms that can tolerate more extreme adjustments without showing annoying side-effects. Also, once again, Lab can’t support high dynamic ranges, so care must be taken to use the Lab modules after HDR tone mapping.

Strictly speaking, the only application where Lab is required is the gamut mapping, when changing color space before sending the image to a file or to the screen. And even then, since 1976, better spaces have been developed (IPT-HDR, JzAzBz) for this purpose, in HDR and with an almost perfect linearity of hues.

The current state of darktable

With the release of darktable 3.0, the default pipeline (i.e. the basic module order) has been reordered around filmic RGB. There are 4 essential steps in this pipe:

1. the demosaic module, which converts the raw file (which only contains the intensity of a single layer, R, G or B at each pixel site) to a picture (with complete RGB data for each pixel location),
2. the input color profile module, which converts the sensor’s RGB space to a standard working color space,
3. the filmic RGB (or the base curve) module, which translates between linear space (proportional to light energy) into non-linear (perceptually compressed) space,
4. the output color profile module, which converts from the standard working space to the RGB space of the screen or the image file.

Note that the base curve approach remains the one applied by default because it allows darktable to more-or-less approximate the rendering of the camera JPEG as soon as the software is opened, which seems to be the preference of many users. Nevertheless, as part of darktable 3.0, the base curve was pushed back in the pixel pipe by default, to just before the filmic RGB module, which makes it safe for the colors produced by the modules that are applied earlier. The base curve module also was provided with a color preservation mode, which produces results similar to filmic RGB. Between base curve and filmic RGB, for darktable 3.0, the difference is now only about ergonomics and on the ability to recover very low light. filmic RGB is a little more complex to understand but faster to set up (once properly understood), and is more powerful when working in deep shadows.

Modules that work in linear RGB and output in linear (thus leaving the pipeline linear after them) are:

1. exposure
2. white balance
3. channel mixer
4. tone equalizer (which is linear in parts).

The advantage of performing linear operations is that they do not affect the chrominance of the image (because changing the luminosity leaves the chrominance intact) and preserve the energy proportionality of the signal. These modules must be positioned before filmic RGB or the base curve. Exposure and tone equalizer are recommended prior to the input color profile. They can be used safely and without moderation. Note that there is a catch here on the tone equalizer, which preserves local linearity (within the image areas), but not the overall linearity (between zones). It corresponds to what would happen if we walked onto the scene with a flashlight, and hand reilluminated the objects in the scene, so we still keep the physical coherence of the signal.

Modules that work in linear RGB and carry out non-linear, but chrominance-preserving operations, (provided that the chroma preservation mode is activated) are:

1. RGB curves
2. RGB levels

The chrominance is preserved via methods that constrain the RGB ratios in and out of the module, so as to keep them identical. Note that RGB curves and RGB levels can be moved before or after filmic RGB depending on the intention, since they’re doing non-linear operations anyway. On the other hand, be careful not to use the mask feathering on modules that come later, as linearity is no longer assured and mask blurring + blending could produce unpleasant results.

Modules that work in linear RGB and carry out non-linear operations without preserving the chrominance are:

1. local tone mapping (we’ll get back to that)
2. color balance
3. LUT 3D

Color balance is designed to be applied to linear RGB data that hasn’t been corrected for contrast, i.e. before filmic RGB, tone curves etc. It does not preserve the chrominance because its explicit purpose is to adjust chrominance creatively. Similarly for LUT 3D, for which the main goal is to emulate analog film emulsions or complex aesthetic transforms.

I remind readers here that filmic RGB is a dynamic range compressor, from the high dynamic range of the camera to the low dynamic range of the screen. It is not a tone curve intended to apply an artistic correction, but a mapping of tones to force fit the sensor data into the the available screen space. filmic RGB tries to protect the details as much as possible (which we assume a priori are in the middle tones) and to keep a certain optical readability in the image.

Before filmic RGB, in the linear pipe, we still find some modules that work in Lab but perform linear operations that should (strictly speaking) be realized in linear RGB:

1. contrast equalizer
2. high pass
3. low pass
4. sharpen
5. denoise (non-local means)

These modules need to be adapted in the future to be able to work on a linear Yxy space (derived from CIE XYZ) because it is a mistake to make them work in Lab (at least, as a default). It’s a relatively easy job to do, because Yxy breaks down the luminance (Y channel) and chrominance (channels x and y) with a logic similar to Lab, minus the non-linear transformation. In the meantime, you can continue to use them, but with moderation. For the contrast equalizer, note that it uses an edge-sensitive wavelet separation, which makes it quite cumbersome to execute, but very effective at preventing halos, even considering that it works in Lab.

After filmic RGB, in the non-linear pipe, are all the other Lab modules, since they require low dynamic range. Some of these modules could also be converted to xyY and moved before filmic RGB in the future (in particular the soften, grain and fill light modules). Also note that the vignette module was left at the end of the pipe, as before, even though it works in RGB. It’s likely it’ll be better off before filmic RGB, or even before the input profile, but its code is surprisingly complex for what it does, and I haven’t had the time to unravel the imbroglio in order to understand what its working hypotheses are.

Modules not recommended

A number of modules are not recommended due to fundamental errors in design (based on my personal opinion, which is based on my practical and theoretical experience in image retouching), and in the spirit of streamlining the workflow with a minimum number of steps. There is nothing stopping you from continuing to use them, especially since users regularly introduce me to new use cases that I hadn’t thought of. But the idea here is to give you the keys to the best possible result as quickly as possible with as little fuss as possible.

Local Tone Mapping

Local tone mapping internally encodes RGB values logarithmically (they are then decoded at the output, so no problem at at this level), then applies a bilateral blur to these logarithmic values. As we saw above, theory is clear: a blur, on anything non-linear, produces halos and fringes. And as promised, the default setting range of this module is much reduced, so that users have become accustomed to merging the output of the module with low opacity – this is only hiding the misery.

Prefer the tone equalizer.

Global Tone Mapping

This module works in Lab color space to perform HDR compression, and if you have followed my explanations, you will understand that this is a contradiction in terms. In addition – and this is important – the white value is adjusted automatically from the maximum in the image, so the overall brightness of the image may change depending on the size of the export, due to the smoothing effect of the setting to scale (interpolation). To be expected: a lighter or darker JPEG than the preview in the darkroom.

Prefer filmic RGB.

Shadows and highlights

Similarly, this module works in Lab color space to perform HDR compression and uses a Gaussian or bilateral blur to isolate highlights and shadows. In practice, it gives halos quickly as soon as you push the parameters (even if the bilateral blurring lessens the problems a little), and it even tends to add local contrast (as a secondary effect) in the highlights, giving clouds a very HDR look. In the shadows, used a little hard, colors turn blue-grey. In practice, it does not work, except for minor corrections.

Prefer the tone equalizer.

Low-pass filter

The low-pass filter is actually a simple blur. A lot of people use it to invert the contrast, and then blend it with overlay or soft/hard/linear light, to compress the dynamic range. This is in fact exactly what the shadows and highlights module already does in fewer steps for the user. As mentioned above, the low-pass module works in Lab color space, so for the blur… Expect the worst.

Prefer the contrast equalizer for blur, or the tone equalizer for local dynamic range compression

High-pass filter

A lot of people use the high-pass module by blending it with overlay or soft/hard/linear light, for adding sharpness. This is in fact exactly what the sharpen module already does. The high pass is achieved by subtracting between a blur (low-pass) and the original image, so we have the same problem as for the low-pass because it’s still working in Lab.

Prefer the contrast equalizer for fine sharpness, or the local contrast for the general sharpness.

Sharpen

The sharpen module was originally intended for sensors with an optical low-pass filter as well as the smoothing due to demosaicing in some cases. First, as this module works in Lab, you need to avoid pushing it so much that it produces halos. Second, the internal sharpening method (using unsharp mask) is rather archaic and quickly artificial, even in RGB mode. Thirdly, in view of the sharpness of modern optics, given that many sensors no longer have low-pass filters, and that most of the photos will be exported at a reduction ratio of at least 8:1 (24 Mpx sensors to 3 Mpx screen), pixel-level sharpness enhancement has become practically useless. Generally speaking, the digital photographer of the 21st century would benefit from calming down with the crisp sharpness - it would be good for everyone.

Prefer to the contrast equalizer to deflect the optics via the presets provided, or the local contrast for general sharpness.

Monochrome

The monochrome module works in Lab, which it uses to define a weighted contribution of certain colours to the density of the black, in order to convert color into shades of gray. The problem is that the interface is quite sensitive to the settings, and a small correction can produce large changes and break the overall contrast in a rather ungraceful way. In practice, getting a predictable result is quite difficult and this module often results in a lot of tedious micro-adjustment sessions.

The idea of a weighted contribution of colors to the density of black comes from silver film, which behaves exactly the same way as this. But, as you saw coming, film doesn’t work in Lab and is not perceptually realistic. This idea is taken up in a physically realistic way in the channel mixer module, where several emulsion presets of commercial silver film are offered to create a grey channel. Note that, in order for the coefficients to be accurate, the colour space of the operating mode (in the module input profile) must be set to REC 709 linear, otherwise the settings will have to be adjusted.

For a black and white treatment that is based on human perceptual luminance (linear), simply lower the input or output saturation to 0% in the color balance module (right-click on the slider and enter 0 on the keypad – the setting is only up to 50% by default in the interface).

Prefer the channel mixer for a silver approach or the color balance for a perceptual approach.

Fill light/Bloom/Zone System

These three modules aim to re-illuminate a part of the image, and attempt to dilute the correction in intensity and in space by blurring in the picture. But since they’re working in the Lab color space …I won’t say it again… The results are just bad all the time, except with very soft settings, in which case you didn’t’ really need those modules in the first place.

Prefer the exposure module with masks, or the tone equalizer

Color Correction

Every photograph has at least two sources of light: a direct source (lamp, sun, candle) and a reflected source (walls, clouds, floors, ceiling). It often happens that the white balance of these two sources does not coincide. In practice, human vision has ways to correct for this this, but not the camera. So it requires a separate white balance correction for the highlights (which generally receive direct light) and the shadows (which usually receive reflected light).

This is what the color correction module offers you, again in Lab color space, and with mixed and unnatural results as soon as you push the adjustment. When you think about it carefully, the white balance can be reduced to discussions of light spectrum, and the correction is simpler in RGB, especially to manage progressively of correction.

The color balance module allows you to adjust this quickly, and not just for the shadows and the highlights, but also for midtones. Using the color-pickers, to the right of the tint sliders, it also allows you to go directly to sample neutral tones in the image (for black, gray and white) and let the software calculate the complementary color. See the manual for more details.

Prefer color balance.

Velvia

Velvia works in RGB and works on a logic quite similar to the color balance saturation. On the surface, it smells good. Except that in fact, its colorimetric equation is not perceptually correct. What it’s doing is changing the saturation (which is its intention), but at the same time it also changes the hue and brightness (which becomes awkward). The problem is that it seems to have been optimized for non-linear RGB. As a result, it is the kind of module that is typically unpredictable.

Prefer color balance.

Levels/RGB Levels

These two are working as they should, no problem with that. But when you look at the code, you can see that it duplicates exactly the slope/offset/power mode of the color balance module. The white point is scaled by a simple exposure correction, such as the slope factor or even the exposure of the exposure module. The black point is adjusted by adding a constant, such as the factor of the offset, or the black level correction of the exposure module. The grey point is adjusted by a power function (sometimes improperly called gamma), just like the power factor of the color balance. They are not just the same features, they are exactly the same math. The difference is therefore not only in ergonomics, but also in the fact that the color balance gives you the numerical value settings, making them more easily transferable from one image to another or from one application to another. Curves and levels also assume you work SDR images, with data encoded between 0 and 1. If you work HDR pictures or raised the exposure quite a lot earlier in the pipe, the pixel values will not be clipped, but the GUI will not give you control over the pixels above 1 (or 100 %).

If you already use the color balance, there is no need to add an additional level module. Finish your retouching in the same module.

Curves/RGB Curves

These also work well, but considering their classic use … are they really useful? Usually they are used to add/remove brightness, which falls in the same use case as the grey of the levels module or the power of the color balance module, or to add/remove contrast, which can be adjusted or by decreasing/increasing the interval between white and black (in a linear way) or by applying a non-linear brightness compression, again available from color balance.

Curve ergonomics is a real problem in an RGB linear workflow, because the middle gray is assumed to be in the center of the graph, which therefore assumes that we are working in non-linear RGB (where the gray at has been increased to 50%). In a linear encoding, the standard medium grey is expected at 18% (but the practice depends or where you anchored your exposure in camera), and the contrast control around this value not being centered on the graph becomes complex in the interface. In addition, the graph of the curves assumes a limited RGB signal between the values 0 and 100% (or 1)… 100% of What? White screen luminance. In a linear workflow, the HDR signal can go from 0 to infinity, and it is at filmic RGB step that we’re in charge of putting everything back between 0 and 100% of the white screen.

The contrast in the color balance module is compatible with this approach using the contrast fulcrum parameter, which allows the selection of the contrast reference. Thus when changing the contrast, we increase the light above the fulcrum, and reduce it below, but the fulcrum remains unchanged. The display workflow (in Lab or non-linear RGB) always has the implicit assumption that gray is 50%, uses it as a contrast reference, and doesn’t allow you to change that value.

Prefer color balance.

Contrast/Brightness/Saturation

Module working in Lab, which duplicates again the modules levels, curves, and color balance while adding undesirable effects on colours.

Prefer color balance.

Modules to be used with care

There is no correct replacement for the following modules for the moment, but they should be used with caution because they can be unpredictable and can cause you to lose a lot of time.

Vibrance

Vibrance works in Lab by applying a saturation correction that penalizes already saturated pixels to avoid over-saturation, but also tends to darken colors. The result is far from ugly, but the problem is that we can’t control how much we darken for the amount we resaturate.

Prefer color zones with a selection by saturation.

Color zones

This module would be awesome if the merging of colour zones were more progressive. It now has two processing modes (strong, the old, and smooth, the new) which are trying to meet this challenge of two different ways, resulting in transitions too discrete for the new, and too abrupt for the old. One more time, it works in Lab, when similar functionality in Capture One seems to be using HSL or HSV, which seems to perform better than Lab.

In some cases, color zones will benefit from being replaced by the color balance module where parametric masking may be used to isolate the shades you want to act on. Then the refinement of the guided filter parametric mask should help in difficult cases. For the rest, color balance allows us to change the shade, saturation and brightness exactly the same.

Note, however, that the color balance module, although working in RGB internally, merges the masks into Lab because this module is older than the possibility to have 100 % RGB modules, and converts from Lab to RGB internally. We’re still working on it…

Prefer color balance.

Vignetting

Adding a vignette around an image is not complicated: you just have to to gradually lower the exposure, and eventually the saturation with a drawn mask. However, the vignetting module performs incomprehensible black magic, which is much more complicated than that, with an internal homogenization which would be superfluous if things were well done. The result is rarely natural, the transition in luminosity being too violent compared to a real vignette.

You will get better results with an instance of the exposure module set to -0.5 EV, a circular mask with a large transition area whose polarity is reversed, possibly coupled with a desaturation in color balance to which you pass the same mask as used in exposure (via a rasterized mask).

Prefer the exposure (and, optionally, the color balance saturation) modules.

Mask blend modes not recommended.

Few people know this, but the blend modes lighten, darken, overlay, soft light, hard light, pin light and linear light implicitly expect the grey level to be 50% grey and are thus totally connected to the display-referred workflow. The blend modes are going to treat the pixels differently depending on whether they are above or below 50 %. Remember that the linear RGB workflow keeps the gray point at 18% (or even less). These blend modes will therefore behave in a way that is unpredictable in the scene-linear portions of the pipe.

In linear RGB, you should only use blend modes based on arithmetic operations (addition, multiplication, division, subtraction, average), on maximum/minimum comparisons (screen) or on channel separations (hue, color, chroma, etc.).

Note that the multiply mode is one of the most powerful in linear RGB. For example, to enhance the contrast of an image in a natural way, it is enough to use an instance of the exposure module blended with multiply. Set the exposure between 2 and 3 EV and the opacity between 10% and 50%. Exposure is then used to control the pivot of contrast, and opacity the intensity of the effect. It’s fast, simple and effective.

A minimal workflow for beginners

In darktable, you can choose between many modules that allow you to do the same thing in a lot of different ways. But this is merely an illusion of choice, as many of them have more disadvantages than advantages (provided you want to achieve predictable results for demanding edits). If you open the code for any of the modules not recommended above, you will see that they are almost all dated 2010-2011 - the only reason we retained them was to maintain compatibility with edits performed in prior versions of darktable.

You can perform at least 80% of your processing with just 4 modules :

1. exposure
2. white balance
3. color balance
4. filmic RGB

The reason they’re so powerful is because they’re actually extremely simple, when you look at their equations:

• Exposure: RGB_output = exposure × RGB_input + black level
• Color balance :
  • Slope/Offset/Power: RGB_output = (slope × RGB_input + offset)^power
  • Contrast: RGB_output = (RGB_input / pivot)^(contrast × pivot)
• White balance: RGB_out = coefficients × RGB_in
• filmic RGB is a little more complex, but it’s still high-school level math

With these 4 modules, you have everything you need to produce a correct image in terms of colorimetry, contrast, and artistic intent. Remember to turn off the base curve if you use the filmic RGB module. Then, if needed, finalize your edit with the following modules:

• To improve sharpness, the best option is the local contrast module in local laplacian mode
• To deblur the lens, you have deblur presets, more or less pronounced in the contrast equalizer
• To denoise, the best algorithm is in the denoise (profiled) module. Use non-local means auto mode if you don’t want to break your head
• To remove haze, you have haze removal
• To convert to black and white, the easiest way is to use the film presets in the channel mixer
• For creative control of overall contrast and re-lighting of the scene a posteriori, use the tone equalizer module

Some of the following modules have an underestimated power, and they are vastly underutilized:

1. The exposure module, with its masks, can replace all the the other methods of mapping HDR, shadows and highlights the tone equalizer, and even the tone curve and the local contrast (to some extent, when used with blend mode multiply)
2. The channel mixer module can overcome all your gamut problems, including problems with blue in stage lighting, without having to use a fake input profile, but also turn grass into snow or summer trees into fall trees
3. The color balance module can allow you to emulate the colors of a film, compensate for uneven white balance, remove redness on the skin, accentuate the depth and shape, create a split-toning effect, or to give an apocalyptic atmosphere to your images

Finally, to display only a minimal selection in the interface and modules, to the right of “More modules“, open the list of presets and select “workspace: all-purpose“.

darktable is a lot simpler when you understand that you don’t have to use all 77 of its modules at once …

If you have any doubts about the order of the modules, you should know that the default order for version 3.0 has been considered globally, and, apart from some uncertainties on the best position of the vignetting and monochrome modules, the rest is pretty solid, in theory and practice.

Conclusion

Pushing pixel values in either direction is one thing. Merging the corrections so they blend seamlessly together on the whole is another. We’ve seen that Lab or non-linear RGB allow the pixels to be pushed more or less correctly, but that it is always when doing mask blending (aka occlusion) and feathering (aka blurs) that we’re paying the price. It turns out there are a lot of blurs under the hood of darktable, sometimes where you don’t expect them. It’s especially problematic when you’re compositing, e.g. inlaying one image within another, to exchange their background without touching the foreground. And it’s precisely this kind of manipulation that led the movie industry to migrate to a scene-referred linear workflow about twenty years ago.

So darktable is in transition. It’s long, it’s sometimes painful, there are a lot of little bits to change in different places along with grumbling users who are hungry for consistency. At least now you know the why and the how. You also know what you have to win. I hope this helps you move forward.

For new users, limit yourself to the above recommended modules, and venture further when you begin to be comfortable. For older users, the new modules have a lot to offer to you, but old Lab modules are still relevant for moderate creative effects and when used with knowledge of their dangers.

The linear toolbox is being expanded. On the agenda:

• rewriting the 100% RGB color balance (including the blending), with the addition of vibrance (and a vibrance equation home-developed to preserve the color)
• conversion of the contrast equalizer and soften modules to the linear xyY space (because in fact, the Orton effect, on which the soften module is based, is very useful when it works correctly)
• a color equalizer, similar to the tone equalizer, which will allow you to adjust saturation, vibrance and Abney effect according to the pixel luminance, to pep up the filmic RGB curve
• a brand-new lens deconvolution module, respectful of the depth of field (but for that, I need to develop a special wavelet based on the guided filter), which should turn your soft 18-55 mm into a Zeiss for much less
• and of course the OpenCL version of the tone equalizer

There is more work than people to do it, so wish us good luck, don’t forget to support us, and Happy New Year 2020 to all of you!

Portrait photographer in Nancy-Metz. Calculation specialist, modeling and numerical simulation for image processing (denoising, deblurring, colour management) and thermal engineering. Developer of filmic RGB, tone equalizer, color balance, and the new themeable interface for darktable 3.0. darktable user since 2010. darktable is my job, so help me out to develop.

G'MIC 2.7 - Process Your Images with Style!

The IMAGE team at the GREYC research laboratory is pleased to announce the release of version 2.7 of G’MIC (GREYC’s Magic for Image Computing), its free, generic, extensible, and probably a little magical, framework for digital image processing.

The previous PIXLS.US article on this open-source framework was published a year ago, in August 2018. This new release is therefore a good opportunity to summarize the main features and milestones of the project’s life over the past twelve months. Fasten your seat belts, the road is long and full of surprises!

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Useful links:

• The G’MIC Project
• G’MIC Twitter Feed
• G’MIC Forum on PIXLS.US

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1. G’MIC in 300 words

G’MIC is a piece of software that has been developed for more than 10 years now, mainly in C++, by two members of the IMAGE team of the GREYC lab: Sébastien Fourey and David Tschumperlé. It is distributed under the terms of the CeCILL free-software license. GREYC is a French public research laboratory located in Caen, specialized in digital sciences, under the head of three academic institutions: CNRS, University of Caen, and ENSICAEN.

The IMAGE team, one of the seven teams in the laboratory, is composed of researchers, professors, Ph.D. students and engineers, all specialized in the fields of algorithmics and mathematics of image processing.

G’MIC is cross-platform (GNU/Linux, MacOS, Windows, …). It provides various user interfaces for manipulating generic image data, i.e. 2D or 3D hyperspectral images or sequences of images with floating-point values (which indeed includes “usual” color images). Around a thousand different processing functions are already available. However, arbitrarily many features can be added thanks to an integrated scripting language.

The most commonly used G’MIC interfaces are: the gmic command, that can be accessed from the command line (which is an essential complement to ImageMagick or GraphicsMagick), the G’MIC Online Web service, but above all, the plug-in G’MIC-Qt, available for the well-known image editing software GIMP, Krita, and Paint.net. It provides more than 500 different filters to apply on images.

Thanks to its extensible architecture, G’MIC is regularly enhanced with new image processing algorithms, and it is these latest additions that will be discussed in the following sections.

2. Add style to your images!

G’MIC has recently implemented a neat filter for style transfer between two images, available from the G’MIC-Qt plug-in under the “Artistic / Stylize“ entry. The concept of style transfer is quite simple: we try to transform an image (typically a photograph) by transferring the style of another image to it (for example a painting).

The implementation of such a style transfer method is relatively complex: The algorithm must be able to recompose the original photograph by “borrowing” pixels from the style image and intelligently combining them, like a puzzle to be reconstructed, to best match the content of the data to be reproduced, in terms of contours, colors and textures. How easily this is done depends of course on the compatibility between the input image and the chosen style. In computer graphics, most existing implementations of style transfer methods are based on convolutional neural networks, more particularly generative adversarial networks (GANs).

G’MIC implements style transfer in a different way (without relying on neural networks, the scientific article detailing the algorithm is currently being written!). This method is parallelizable and can therefore benefit from all the processing units (cores) available on the user’s computer. The computation time naturally depends on the input image resolution, and the accuracy of the desired reconstruction. On a standard 4-cores PC, it could take tens of seconds for low resolution images (e. g. 800x800), up to several minutes for larger pictures.

As one can imagine, it is a very versatile filter, since we can apply any style to any input image without hard constraints. Some famous paintings are available by default in the filter, in order to propose predefined styles to the user.

Let us be honest, it is not always easy to obtain satisfactory results from the first draft. It is generally necessary to choose your starting images carefully, and to play with the many parameters available to refine the type of rendering generated by the algorithm. Nevertheless, the filter is sometimes able to generate quite interesting outcomes, such as those shown below (the original photo is visible at the top left, the style chosen at the top right, and the result of the style transfer at the bottom). Imagine how long it would take for a graphic designer to make these transformations “by hand”!

Other examples of image stylization can be found on the image gallery, dedicated to this filter. To our knowledge, G’MIC is the only “mainstream” image processing software currently offering a generic style transfer filter, where any style image can be chosen.

A last funny experiment: get a picture of an Alien’s head, like Roswell, and then select a crop of the Mandelbrot fractal set as your style image. Use the transfer filter to generate a “fractal” rendering of your alien head. Then, make the whole world believe that the Mandelbrot set contains the mathematical proof of the existence of aliens… ☺

In short, this filter has a clear creative potential for all kind of artists!

3. Interactive deformation and morphing

This year, G’MIC got an implementation of the RBFs interpolation method (Radial Basis Functions), which is able to estimate a dense interpolated function in any dimension, from a known set of scattered samples (not necessarily located on a regular grid). Thus, it gave us the idea to add distortion filters where the user interaction is focused in adding and moving keypoints over the image. In a second stage, G’MIC interpolates the data represented by these keypoints in order to perform the distortion on the entire image.

Let us start with the “Deformations / Warp [interactive]” filter which, as its name suggests, allows the user to distort an image locally by creating/moving keypoints.

The animation below shows this interactive filter in use, and illustrates the fact that these keypoints can be considered as anchors to the image, when they are moved.

(For those who might be concerned about the portraits photos used in the figures above and below: all these portraits are totally artificial, randomly generated by GANs via the website This Person Does Not Not Exist. No moral prejudices to dread!).

The great advantage of using RBFs-based interpolation is that we do not have to explicitly manage a spatial structure between the keypoints, for instance by defining a mesh (i.e. a “deformation grid”). This gives a greater degree of freedom in the obtained distortion (see Fig.3.3. below). And at the same time, we keep a rather fine control on the local amplitude of the applied distortion, since adding more “identity” keypoints around a region naturally limits the distortion amplitude inside this region.

A short demonstration of this distortion filter is also visible in this Youtube video.

And why not extending this kind of distortion for two images, instead of a single one? This is precisely what the new filter “Deformations / Morph [interactive]” does. It is able to render a morphing sequence between two images (put on two separate layers), using the same interpolation technique that only asks for the user to set colored keypoints which match on both images.

In the example above, keypoints are placed on characteristic areas of both faces (tip of nose, lips, eyebrows, etc.). In practice, this takes no more than 5 minutes. Thanks to these keypoints, the algorithm is able to estimate a global deformation map from one image to another, and can generate temporally “mixed” frames where the elements of the face remain relatively well aligned during the whole morphing sequence.

By comparison, here is what we would obtain by simply averaging the two input images together, i.e. without correcting the displacement of the facial features between both images. Not a pretty sight indeed!

Thus, the morphing filter is able to quickly generate a set of intermediate frames, ranging from the “Source” to the “Target” faces, a sequence that can then be saved as an animation.

Many other use cases of this morphing filter can be considered. The following example illustrates its application to render an animation from two photographs of the same object (a garden gnome), but shot with different DOFs (Depth of Field).

Command line users will be pleased to know that these two filters can be tested very quickly from a shell, as follows:

$ gmic image.jpg x_warp
$ gmic source.jpg target.jpg x_morph
~

4. Ever more colorimetric transformations

For several years, G’MIC has contained colorimetric transformation filters able to simulate the film development process, or to give particular colorimetric moods to images (sunlight, rain, fog, morning, afternoon, evening, night, etc.). In a previous report, we already mentioned these filters, which are essentially based on the use of 3D CLUTs (Color Lookup Tables) for modeling the color transformation.

A 3D CLUT is technically a three-dimensional array that provides for each possible RGB color, a replacement color to apply to the image.

The main interest of these 3D CLUTs is the great variety of transformations they can represent: They can indeed define RGB-to-RGB functions with almost any kind of variations. The only “constraint” of these methods is that all image pixels having the same color will be transformed into pixels that also have an identical color.

The disadvantage, however, is that these 3D CLUTs are relatively data intensive. When you want to embed several hundred different ones in the same piece of software (which is the case in G’MIC), you quickly find yourself with a large volume of data to install and manage. For instance, our friends at RawTherapee offer on their website an additional pack of 294 CLUTs functions to download. All these CLUTs are stored as .png files in a .zip archive with a total size of 402 MB. Even if downloading and storing a few hundred _MB_ is no longer limiting nowadays, it is still quite large for things as simple as color changing filters.

This year, we have therefore carried out important research and development work at the GREYC lab on this topic. The result: a new lossy compression algorithm (with visually imperceptible compression losses) that can generate binary representations of CLUTs with an average compression rate of more than 99%, relative to the data already loslessy compressed. The general idea is to determine an optimal set of color keypoints from which the CLUT can be reconstructed (decompression), and this, with a minimal reconstruction error.

As a result, this original compression method allowed us to offer no less than 763 CLUTs in G’MIC, all stored in a binary file that weights less than 3 MB !

All these color variation filters have been grouped into two separate entries in the G’MIC-Qt plug-in, namely “Colors / Simulate Film” (for analog film simulations), and “Colors / Color Presets” (for other color transformations). Each of these filters provides sub-categories for a structured access to the hundreds of CLUTs available. To our knowledge, this makes G’MIC one of the image processing software with the most colorimetric transformations, while keeping a reasonable size.

Readers interested in the mathematical details of these CLUT compression/decompression algorithms may refer to the scientific paper we wrote about it, as well as the presentation slides that have been presented at the conferences GRETSI’2019 (French conference, in Lille) and CAIP’2019 (International conference, in Salerno).

To finish with this topic, note that we have made an open-source implementation of our decompression algorithm of CLUTs available online (in C++, with 716 CLUTs already included). Discussions have also been initiated for a potential integration as a Darktable module for managing 3D CLUTs.

5. Create palettes by mixing colors

Let us now talk about the recent “Colors / Colorful Blobs” filter which is directly inspired by the original concept of Playful Palette created by the Adobe Research team in 2017. This filter is intended for illustrators (designers and digital painters). The goal: Create color palettes which contain only a few main colors (the ones you want to use in an illustration), but also a few sets of intermediate shades between these colors, in the form of color gradients. An artist is theoretically able to better preserve the color coherence of its artwork, by picking colors only from this palette.

As shown on the figure above, the filter allows the artist to create and move colored “blobs” that, when merged together, create the desired color gradients. The result of the filter is thus an image that the artist can use afterward as a custom 2D color palette.

From a technical point of view, this filter is based on 2D metaballs to model the color blobs. Up to twelve separate blobs can be added and different color spaces can be chosen for the calculation of the color gradient (sRGB, Linear RGB or Lab). The filter also benefits from the recent development of the G’MIC-Qt plug-in that enhances the user interactivity inside the preview widget (a feature we mentioned in a previous report), as seen in the animation below (see also this longer video).

This filter may not be useful for most G’MIC users. But you have to admit, it’s pretty fun, isn’t it?

6. Some more filters

Let us now describe a selection of a few other filters and effects added during the year, perhaps less original than the previous ones (but not completely useless anyway!).

• First of all, the “Rendering / Symmetric 2D Shape” filter is a great help when you want to draw geometric shapes having angular symmetries.

  

  The plane can be subdivided into up to 32 angular pieces, each of which can contain a maximum of six keypoints to define a shape profile, allowing potentially complex and varied shapes to be rendered (such as the super-shuriken below!).

  

• The “Degradations / Self Glitching” filter combines an image with a shifted version of itself, to create a Glitch-art type image. Several bitwise operations (Add, Mul, And, _Or_, Xor,…) can be chosen and you can adjust the shift direction and amplitude, as well as various other controls.

  

  Again, this is not a filter that will necessarily be used every day! But it may be helpful for some people. It was actually added in response to a user request.

• In the same style, the “Degradations / Mess With Bits” filter applies some arithmetic operations to the pixel values, seen as binary numbers (for instance, bit shift and bit inversion). Always with the idea of rendering Glitch art, of course!

  

• The “Degradations / Noise [Perlin]” filter implements the generation of the Perlin noise, a very classical noise model in image synthesis, used for the generation of elevation maps for virtual terrains. Here we propose a multi-scale version of the original algorithm, with up to four simultaneous variation scales.

  

• The “Frames / Frame [Mirror]” filter is also a “tailor-made” effect, to meet the needs of a G’MIC-Qt plug-in user. This photographer wanted to resize his photos to obtain a precise width/height ratio, but without having to crop his images. The solution was instead to add image information at the edges of the picture, by symmetry, in order to obtain the desired ratio. So that’s what this filter does.

  

• Finally, let us mention the upcoming advanced image noise reduction filter, by Iain Fergusson, whose development is still in progress. Iain has been contributing to G’MIC for several years now by implementing and experimenting original denoising filters, and his latest project seems really interesting, with promising results. This video shows this filter in action, a good place to learn a little more about how it works.

Now that we’ve looked at these new filters, it seems important for us to remind that, as in many IT projects, this visible part of the iceberg hides a set of lower-level developments done to improve the interactive possibilities of the G’MIC-Qt plug-in, as well as the performance of the internal scripting language interpreter (the G’MIC language), which is how all these filters and effects are actually implemented. These improvements and incremental slight optimizations of the code base benefit to all filters (even those already available for several years) and it actually represents most of the development time we spend on G’MIC. So, dear users, do not be surprised if no new filters appear for a while. It is probably just because we are doing serious work on the G’MIC framework core!

7. Other notable points in the project life

Here are listed some other important news that have punctuated the life of the project since August 2018.

7.1. We now accept donations!

This is essential news for us: since March 2019, the G’MIC project has been granted permission to collect donations (via Paypal), to help in its maintenance and development!

This is a good thing, because until now, there was no simple way for a public research laboratory as the GREYC, to accept donations for supporting the development of a free software application such as G’MIC, an application used daily by several thousand people around the world. And we have currently no other ways to finance this piece of software in the long term.

Thus, we have partnered with LILA (Libre comme l’Art), a French non-profit organization promoting Arts, Artists and Free Software, who accepted to collect donations for us.

In practice, this is something that has been a little long to set up, but now that the donation system is operational, we hope to benefit from it in the future to make the project development even faster (the possible use of the raised funds is detailed on the donations page, this being of course very dependent on the amount of money collected).

For the sake of transparency, we will post the monthly amount of collected donations on the project website. At this point, we don’t really know what to expect in practice. We will see how these donations evolve. Of course, we would like to thank all those who have already participated (or plan to do so) in supporting our open-source framework for image processing. Our ultimate dream would be, one day, to say that the illustration below is only a distant memory!

7.2. Integrating “Smart Coloring” into GIMP

Let us also mention the work of Jehan, known to PIXLS.US readers as a regular GIMP developer. Jehan has been hired by the GREYC laboratory in September 2018, to work on G’MIC (for a 12-month fixed-term contract), thanks to a grant funded by the INS2I Institute of the CNRS (for which we are grateful).

One of its first missions was to re-implement the G’MIC “Smart Coloring” algorithm (that we had already talked about previously) as a new interactive mode integrated into the existing GIMP “Bucket Fill“ tool.

Jehan described all his work in a blog post, which is strongly recommended for reading. Of course, we don’t want to copy his post here, but we want to mention this activity, and to consider it as another original contribution of the G’MIC project to free software for graphic creation: at the GREYC laboratory, we are really happy and proud to have imagined and developed an image colorization algorithm, which artists can use through a well integrated tool into such a popular piece of software as GIMP!

This intelligent colorization algorithm has been the subject of scientific publications, presentations at the conferences GRETSI’2017, EuroGraphics VMV’2018, as well as at the Libre Graphics Meeting’2019. And it is with a great pleasure we see this algorithm is used in real life, for various realizations (as in this great video of GDQuest, for colorizing sprites for video games, for instance).

Scientific research carried out in a public laboratory, which becomes available for the general public, that is what we want to see!

7.3. Other news related to the G’MIC project

• Recently, a major improvement in the performances of G’MIC under Windows has been achieved, by recoding the random number generator (now reentrant)) and removing some slow mutex which were responsible of performance drops for all filters requiring sequences of random numbers (and there were many!). As a result, some filters are accelerated by a factor of four to six under Windows!

• Since December 2018, our G’MIC-Qt plug-in is available for Paint.net, a free graphic editing software application under Windows (not open-source though). This has been possible thanks to the work of Nicholas Hayes who wrote the glue code allowing the interaction between our G’MIC-Qt plug-in and the host software. Users of Paint.net are now able to benefit from the 500+ filters offered by G’MIC. This plug-in, available here, has already been voted “Best Plug-in of the Year 2018“ by the members of the Paint.net forum ☺ !

• Since October 2018, the G’MIC-Qt plug-in for GIMP has been compiled and proposed for MacOS by a new maintainer, Andrea Ferrero, who is also the main developer of the free software application Photoflow, a non-destructive image editor (more information here). Many thanks Andrea, for this wonderful contribution!

  • Since the announced shutdown of the Google+ social network, we have opened two new accounts, on Framasphere and Reddit, to share news about the project’s life (but the Twitter feed is still our most active account).

• Let us also thank Santa Claus, who kindly brang us a materialized version of our mascot “Gmicky” last year. That looks almost perfect!

  

• The G’MIC project was presented at the FENO, the “Fête de l’Excellence Normande“, from 12 to 14 April 2019, at the Caen Exhibition Centre. We were hosted on the stand of the CNRS Normandie, and we carried out demonstrations of style transfer (teaser) and automatic illumination of clip arts (teaser), for the general public.

  

• And to dig even deeper, here are some other external links we found interesting, and which mention G’MIC in one way or another:
  • A video presentation of the plug-in G’MIC-Qt, by Chris’ Tutorial;
  • The Youtube channel MyGimpTutorialChannel offers a lot of videos showing how to use G’MIC-Qt in GIMP to achieve various effects (mostly in German);
  • The Clinic, a Chilean weekly newspaper, apparently used G’MIC to achieve an effect on one of its covers (via the smoothing filter “Artistic / Dream Smoothing”);
  • Another video tutorial, showing how to use the G’MIC “Artistic / Rodilius” filter to create stylized animal photos.

8. The future

As you see, G’MIC is still an active open-source project, and with its 11 years of existence, it can be considered as mature enough to be used “in production” (whether artistic or scientific).

We have never defined and followed a precise roadmap for the project development: the functionalities come according to the needs of the developers and users (and the limited time we can devote to it!). At the moment, there is a lot of interest in image processing methods based on neural networks, and deep learning techniques. It is therefore possible that one day, some of these methods will be integrated into the software (for instance, we already have a prototyped code running in G’MIC that actually learns from image data with convolutional neural networks, but we are still at the prototyping stage…).

After 11 years of development (make it 20 years, if we include the development of the CImg library on which G’MIC is based), we have reached a point where the core of the project is, technically speaking, sufficiently well designed and stable, so as not to have to rewrite it completely in the next years. In addition, the number of features available in G’MIC already covers a large part of the traditional image processing needs.

The evolution of this project may therefore take several paths, depending on the human and material resources that we will be able to devote to it in the future (for the development, but also in project management, communication, etc.). Achieving an increase in these resources will undoubtedly be one of the major challenges of the coming years, if we want G’MIC to continue its progress (and we already have plenty of ideas for it!). Otherwise, this image processing framework might end up being just maintained in its current (and functional) state. It is of course with a hope for progression that we have recently set up the donation page. We also hope that other opportunities will soon arise to enable us to make this project more visible (you are invited to share this post if you like it!)

That’s it for now, this long post is now over, thank you for holding on until the end, you can resume normal activity! I’ll be happy to answer any questions in the comments.

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Post-scriptum: Note that the 3D animation displayed as the teaser image for this post has been actually generated by G’MIC, via the command $ gmic x_starfield3d. An opportunity to remind that G’MIC also has its own _3D_ rendering engine capable of displaying simple objects, which is very practical for scientific visualization! We may have the occasion to talk about it again in a future post…

A special thank you for reviewing and helping to translate this article to:
Patrick David, Sébastien Fourey, Christine Porquet, Ryan Webster.

Quick digiKam Tip: Back up digikamrc file

digiKam stores the current state of the application in the ~/.config/digikamrc file. This file keeps track of pretty much everything: from the database connection profile and custom toolbar settings, to the last-used curve and sharpening parameters. So next time you install or reinstall digiKam, don’t forget to back up the digikamrc file. This way, you don’t have to configure a fresh digiKam installation from scratch. Simply copy the file to a safe location or external storage device, and drop the file into the ~/.config folder before you run digiKam.

Location Tracking for Photographers with GPS Logger and Trekarta

When it comes to Android apps for photographers, we are spoiled for choice. From depth-of-field and golden hour calculators to sun position and remote control apps – there are plenty of clever tools to choose from. But there is one particular app combination that can prove to be indispensable for any photographer on the move: a GPS logger and a GPX viewer. There are two main reasons for that.

1. Tracking your movements and saving them in the GPX format can come in handy for geotagging photos.

2. The ability to attach comments to the current location allows you to use the GPS logging app to note places you either photographed or you plan to photograph later. You can then use a GPX viewer app to see and manage bookmarked locations.

There are several apps that offer GPS logging and viewing, but you can’t go wrong with GPS Logger for Android and Trekarta. Both apps are released under an open source license, and they are available free of charge on Google Play and F-Droid.

How you set up GPS Logger for Android is a matter of personal preference. One way to go is to configure the app to automatically start tracking on boot and upload tracks to the desired destination (e.g., a NAS or a file sharing service).

Once GPS Logger for Android is running, adding a comment to the current location is as easy as pulling down the notification drawer and tapping Comment. The app saves the tracks as GPX files in the Android/data/com.mendhak.gpslogger/files directory on your Android device. To view a GPX file in Trekarta, use a file manager to navigate to the directory, and use Android’s sharing functionality to send the desired GPX file to Trekarta.

Processing a nightscape in Siril

A basic tutorial

Siril is a program for processing astronomical photographs.

In this tutorial, I’ll show you how to process a nightscape in Siril 0.9.10.

It doesn’t intend to be comprehensive tutorial but rather to present a basic general workflow that is a good starting point for those who want to learn Siril.

For this purpose, I’m sharing the raw files I used for the image I presented here, except that for this tutorial I limited the number of frames for the sake of bandwidth and processing speed.

You can find and download the raw files here (~1GB).

Setup

The raw files are placed in specific sub-folders according to their use:

• bias/offset frames → ./Bias (20 files)
• dark frames → ./Darks (15 files)
• flats field frames → ./Flats (15 files)
• main subject/light frames → ./Lights (10 files)

Bias, dark, and flat field frames are also called “calibration” frames, their purpose being to improve the quality of the image by correcting the signal-to-noise ratio (in the case of bias and dark frames) and vignetting (with the flat frames). There are several places where you can learn more about the different types of frames for astrophotography.

At the root of the folder, I placed two text files with the .ssf extension, these are scripts used by Siril for batch processing the files. Quite useful. If you want to run a script from Siril, place the .ssf files in ~/.siril/scripts. Upon restarting Siril, a new Scripts menu appears in the top menu bar, allowing you to launch the installed scripts.

I suggest you download the whole folder (~1GB), and move the scripts as indicated above. This way, if you set the working directory in Siril to the root of the folder, launching the script named processing_from_raw.ssf will automagically process the raws and create the output image in both .fit and .tif (16-bit) formats. Please note that in order to successfuly run the scripts, there must be a folder structure like the one used in this tutorial.

Step-by-step processing

I will present the steps I used to process an image of the Milky Way. I don’t know if it’s the best way, but it’s probably close to what the developers of Siril advise to do for the general case of starting from raw files (actually, I started from one of their scripts and just slightly adapted it).

We will start with processing the calibration files, and then processing the lights.

Preparing the bias frames

1. Set the working directory to the Bias sub-folder by clicking on Change dir….

2. We will use the 20 bias frames to generate a master-bias frame. To load the bias frames, click on the + button as shown (make sure that you select RAW DSLR Camera Files in the combo box) and select the bias frames located in the Bias subfolder.

3. In the “Sequence name” field, enter bias (or whatever you see fit) to set the prefix of the sequence and subsequent files, and click Convert to convert the files to the FITS format, which is the main format used by Siril. Note that you don’t need to demosaic the files yet, make sure the Debayer box is unchecked.

When done converting the bias frames, a window will pop up showing a preview of one of the bias frames. Note that since it’s not demosaiced, it will only show as a B&W channel image.

At this point, the bias frames are loaded and ready to be processed to make a master-bias frame.

4. In the Stacking tab, choose Average stacking with rejection as stacking method, and No normalisation under the normalisation combo box. You can leave the Sigma parameters at their default (unless you know or want to experiment for better values).

It should look like this:

5. Click on the Start stacking button. The resulting master-bias frame will be saved as bias_stacked.fit in the Bias subfolder.

Preparing the flat field frames

Since the flats also contain the sensor readout noise (contained in the bias frames), we should remove it by subtracting the master-bias.

1. In the File conversion tab, remove the files already loaded by clicking on the button located just below the - (minus) button, and by clicking on the + (plus) button select and load the flat frames located in the Flats subfolder.

2. Set the working directory to the Flats sub-folder by clicking on “Change dir…” and set the Sequence name as “flats”.

3. Like for the bias frames, ensure Debayer is unchecked, then click on Convert.

4. In the Pre-processing tab, check only the Use offset box, click on Browse to select the Bias/bias_stacked.fit file, and click on Start pre-processing.

5. To generate the master-flat, go to the Stacking tab, and this time set Normalisation to Multiplicative and the Stacking Method as Average with rejection.

6. Click on Start stacking to produce the pp_flat_stacked.fit master-flat frame in the Flats subfolder.

Preparing the dark frames

As with the bias and flats, you need to load the dark frames.

1. In the File conversion tab, remove the files already loaded, select and load the dark frames located in the Darks subfolder.

2. Set the working directory to the Darks sub-folder by clicking on Change dir…, and set Sequence name as darks.

3. Debayer should be unchecked.

4. Click on Convert.

5. The darks need to be stacked the same way as the bias frames. In the Stacking tab, choose Average with rejection and No normalisation.

6. Click Start Stacking.

The master-dark frame is saved as Darks/dark_stacked.fit.

Note: if you take images often in the same conditions (same air temperature, same exposure settings), you can save the dark_stacked and pp_flat_stacked files, and re-use them to process future light frames faster. I read on some forums that some astrophotographers keep their calibration files and use those for around 1 year, before taking new calibration frames.

Preparing the light frames

Now it’s time to start processing the light frames, by first subtracting the darks (which also contain the bias signal) and the flats (from which bias has already been subtracted).

1. Select the light frames in the File conversion tab.

2. Set the Sequence name to lights, and point the working directory to the Lights (Change dir...).

3. Convert the files, still without debayering.

4. Then go to the Pre-Processing tab, check Use dark, select the Darks/dark_stacked.fit file, check Use flat, and select the Flats/pp_flat_stacked.fit file.

5. Make sure that the other boxes are checked as in the following screenshot.

Note that “Cosmetic Correction” can also be done from the “Image Processing” tab.

6. Click Start pre-processing.

This will produce new FITS files with the prefix pp_light_ and the corresponding .seq file. These files are loaded.

Demosaicing the files

It’s time to demosaic our processed files. There’s something strange in the GUI, in that after pre-processing, when you uncheck “Use dark” and “Use flat” boxes, the “Debayer FITS images before saving” and the “Start pre-processing” button become grayed out.

1. In the File conversion tab, remove the selected files and load the 10 pp_light_000xx.fit files.
2. Check the Debayer box and write db_pp_light as the sequence name.
3. Click Convert.

The pre-processed lights will be saved as FITS files, and the corresponding db_pp_light.seq file loaded. Two preview windows will open this time, one with the 3 RGB channels separated, and one with the RGB composite image.

4. In the Register tab, select Global Star Alignment (deep-sky) from the registratrion method drop-down list and click Go register.

If you have more 8GB of RAM, you can try checking the Simplified Drizzle x2 box (it will up-sample the images by a factor 2, increasing the RAM usage by a factor 4). Siril will detect the stars and register each of the 10 images. The preview windows will be updated. By the way, you can play with the zoom and select AutoStretch to get a better preview of the selected image.

5. In the Stacking tab, make sure that Average with rejection is selected as the stacking method, and that Additive with scaling is set for Normalisation.

6. Click on Start stacking.

The resulting aligned and stacked image will be saved as Lights\r_db_pp_light_stacked.fit.

7. At this step, you can also save the resulting image as JPEG, TIFF, PNG, etc. for further processing in your favorite image editor. On the menu, just click on File > Save As, and pick the image format you wish (or right-click on the RGB windows and pick the format that best suits you).

Post-processing the image

Siril can do some more or less specialized post-processing to your image. I found it interesting to use.

• While the stacked image is still loaded in Siril, you can apply a log transform (it is in linear mode in Siril). I haven’t found how to do it in the GUI, but you can simply type “log” in the “Console” field at the bottom of the Output logs tab, in the main window.
• Still in the console field, you can use the command “crop” followed by the coordinates of the bounding box in pixels, to crop the image (some auto-detection tools in Siril require the image to be cropped to remove the borders introduced by aligning the images, in order to work properly). For example, my image can be cropped by typing crop 30 30 5950 3970.
• You can apply green noise removal in the “Image Processing” tab > “Remove Green Noise…”.
• Lucy-Richardson deconvolution can be applied in “Image Processing” menu option > “Deconvolution…”. 10 iterations and a Sigma value of 0.6 are a good starting point.

The resulting image can be saved as JPEG, TIFF, PNG, etc. for further processing in your favorite image editor or as a finished image if you’re satisfied.

Processing for the foreground

The problem with this whole process, is that because the images have been aligned with the stars as reference, the foreground will be blurred because earth moved between successive frames. What I do is to reprocess the light frames from just after the calibration step (i.e. after the dark and flat frames subtraction) but only skipping the stars registration step. By doing so, the foreground will undergo the same pre- and post-processing, and the resulting image will have a sharp foreground and trailing sky.

I provided a script (processing_from_raw_foreground.ssf) which will do that for you, if you already used the first script or if you use the same file naming convention as in the script.

Finally, in your favorite image editor, you can combine the “sky” and “foreground” images using a mask, to get both the sky and the foreground sharp.

Here’s what I obtained following these steps (but using the scripts), after just combining the 2 images in Gimp:

And after quick curve and saturation tweaking in Gimp: