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Automatic illuminant correction
The intent is usually to take out any mild tint that the illuminant has, or to correct a camera's mis-estimation of the illiminant.
In other words, mainly white balance correction, a.k.a. gray balance.
Color correction often comes down to
- estimating what the illuminant probably was
- photographers who cares about color accuracy tend to use a gray card to have a known-absolute reference.
- without one, it's based on assumptions
- and incorrect assumptions can introduce unnatural tinting in the process
- apply chromatic adaptation so that that illuminant effectively becomes a given illuminant, such as D65 (mid-day sun), or just numerically equalize channels.
There are some more specific cases you could focus on, such as cases where you know color filters were used, or e.g. faded photographs could consider the dyes in use - their relative fading is usually documented.
The idea: in a well balanced image, the average color is a neutral gray.
So the correction scales channels to makes the average become gray, typically implemented by making the average of the red, green, and blue the same, often by a basic linear gain.
The results vary a little with in which color space you do this.
Gray world makes decent sense when large patches are expected to be neutral, which is e.g. true in darker photographs.
- works well on removing the illuminant's tint on images with a lot of white, a lot of dark areas, and/or a lot of each color, or a photograph which had such a neutrality but had a tint applied to it
- Only true for images with a roughly white illuminant
- if tinted, that you want to fully negate that tint - e.g. images intentionally taken with red light will effectively just ramp up Green and Blue - of which there was very little, so it likely comes out quite unnatural
- can be tempered by limiting that gain
- effectively equally uses all of the image for measurement
- an assumption that is flawed to varying degrees
The idea: The brightest color should be white, the darkest color should be black.
So: rescale each channel's histogram to span the full range, accepting that some (e.g. 1%) will lie outside and be truncated.
- brightest color becomes white, which on outside photos is often good enough
- less sensitive to single-color use than gray world
- suffers from similar problems to gray world
- not sensitive to how much small the brightest pixel-area is. For example, having a bright window in the background means a white wall will be ignored.
In the context of whole-image illuminant/color correction, it mainly says that perceived white tends towards the strongest cone signal.
This is essentially a gentler form of gray world, referring to the overall effects rather than a single spot.
It roughly means that the maximum within each channel should be the same. While image-pixel-RGB doesn't correspond precisely to eye perception, it's close enough to work well.
The correction could be implemented as just (linear) gain on each channel to make the maxima the same, though in practice, using a very-high-percentile point, to ignore a few outlier pixels, is more robust.
- simple idea, simple code
- better behaved than gray world, in that it avoids many larger color shifts
- sometimes too cautious, e.g. does little on overexposed images
- because it still has the underlying assumption the illuminant must be white-ish, it breaks
- on scenes that had such an illuminant but no near-illuminant color in it
- where the illuminant is colored, e.g. underwater photography
- can be tempered by limiting the difference between the different gains applied - because usually significant different gains makes no sense
- ...though that in combination with the percentile logic can have some odd side effects
Gray world plus retinex
"Combining Gray World and Retinex Theory for Automatic White Balance in Digital Photography" argues that combining the two makes sense.
Which requires a little trickery, as linear correction alone cannot satisfy both criteria at once.
Robust Automatic White Balance
Essentially a variant of gray world that is selective about the areas it uses, primarily looking for nearly-white parts, so e.g isn't distracted by the average of the colored parts.
- Doesn't make as many mistakes as plain gray world
- photos taken outside usually have such near-whites, so this makes sense for them
- images may not have representative near-whites
- selection of areas to use turns out to be harder than it sounds, depending on how robust you want it to be.
A Rizzi et al., "A new algorithm for unsupervised global and local color correction"