Retinex

Note: This translation of the "Retinex" French page is a working document.

Generalities

While the eyes are able to see correctly the colours through a poor lighting, a coloured surrounding or a veil of fog, cameras badly manage in theses conditions. It is by copying the eyes biological mechanisms to adapt itself to these conditions that the MSR algorithm (MultiScale Retinex) has been created. In addition to the digital photography, the Retinex algorithm (Retinex is the contraction for Retina + Cortex) is used in astronomy to show up information laying in the astronomical photographs, in medicine to detect not much visible structures in radiography and tomodensitometry. Numerous theories and algorithms have been working out for more than 20 years. The first experimentation has been proposed by Rahman in 1996. The approach follows the human visual perception and the Edwin Land's retinal function. In a way, this approach is pretty similar to CIECAM. This function and more particularly its general form is similar to a DOG (Difference Of Gaussian). The idea consists of characterizing the luminous information of a point from its intensity and the intensity of its neighbours. This said, this approach has no scientific basis and is only lying on experience and various empirical constants. Over the years many improvements have been added, but from my own point of view, no one is fully satisfactory. I relied on two documents :

• "Automatic Image Haze Removal Based on Luminance Component" (Fan Guo, Zixing Cai, Bin Xie, Jin Tang"[1]
• "Retinex Algorithm on Changing Scales for Haze Removal with Depth Map" (Weixing Wang, Lian Xu)"[2]
• and from some programming tricks inspired from "2003 Fabien Pelisson <Fabien.Pelisson@inrialpes.fr>"

The use of Retinex might be beneficial to images processing:

• that are hazy, misty or having a veil of fog
• with important luminance gaps
• where the user looks for special effects (tone mapping …)

Retinex at the beginning of the processing

It is the algorithm described in this page, with its limitations, advantages and drawbacks.

Retinex in "Wavelets"

I installed 2 possibilities to enable Retinex in the Wavelet process: Wavelet levels/fr. Some of the quoted limitations are no more there !

Quick comparison between the both versions " Retinex in Wavelets" and " Retinex at the beginning of the processing": Wavelet_levels/fr#Avantages_.28.2B.29_et_inconv.C3.A9nients_.28-.29_de_Retinex.2Ffr_par_rapport_.C3.A0_Retinex_in_wavelet

Imposed limitations by RawTherapee

The basic algorithm impose a reference to the whole image, and not to a crop or a reduction like in the RawTherapee processing. This limitation imposed by the gaussian function causes several consequences:

• The processing has been shifted from its dedicated place, that should have been close to "Lab Adjustments" - near the beginning of the RawTherapee processing – that is necessarily (except if somebody has an idea to do different) a raw process. As a consequence, non raw files (TIFF, JPG, …) can't be processed with this algorithm? This problem should be solved soon (November 2015 ??).
• The second consequence of this position is that the characteristics of the raw data situated just after demosaicing are very different that the ones situated downstream: no gamma, no gamut limitation, no white balance, no RGB conversion… so we must expect artefacts and poor luminance and colour rendering.
• The third consequence is the system response time which imposes to re-actualize the whole process after each change in the settings.
• The fourth, sometimes, will need a white point modification, in order to avoid colours distortions (for example: a magenta sky).
• But advantage, this process being situated before "Denoise", the noise reduction will be here fully effective.

Nevertheless, despite these handicaps, as we will see it further, results are more than satisfactory as well about processing time that in contrast and colours rendering, particularly by joining Retinex and Wavelets actions.

Algorithm principle

For more information, see the "pdf" documents given in the "Generalities" section.

To elaborate a "Transmission Map" file obtained by

• making the difference, for luminance only, between each pixel logarithm of the input image and the neighbouring pixels logarithm of the matching gaussian image. The standard deviation (gaussian function) used here is very high - usually in RawTherapee sigma values from 0.5 to 5 are common – here the values range from 10 to 280 (Single Scale Retinex), it is the user choice.
• modifying the input image luminance distribution by

• applying a gamma before the "Transmission Map" file creation
• applying an inverse gamma to restore the input image characteristics

This modification of the distribution allows:

• to change the image tonality
• to modify the "Transmission Map" file to take into account for example the under or over exposed areas.

• applying several time (Scale) – 3 times, not modifiable by the user – this algorithm with an addition using empirical coefficients (Multi Scale Retinex). (Low scale values increase the apparent contrast, but give a perspective look to the image, high scale values make the image more natural but they have a tendency to increase the noise).
• a variation of MSR by the user according to the desired effect (Uniform, Low, High, Highlight)
  • Uniform: Strive to process low and high intensities in a balanced way.
  • Low: Improve low intensities areas
  • High: Improve the rendering of the more exposed areas
  • Highlight: Improve the rendering of the highlight areas that may become magenta, but can bring strong artefacts.
• choosing the colorspace
  • logarithmic L*a*b*
  • logarithmic HSL
  • linear HSL – this version doesn't comply with Retinex algorithm but in some cases it allows a more suitable image processing.

So, we get a logarithmic distribution "Transmission Map" – except in linear mode! - that we will be able to "subtract" to the input image, either to get an image theoretically free from mist and from veil fog or to get special effects. This distribution owns approximatively a gaussian distribution with a minima (minT) about, according to the images – higher for images with veil – from -10 to -40, an average close to from -1 to +2, a standard deviation often about 2 to 6 and a maxima (maxT) about 10 to 40. The negative values mean low intensity and positive values mean high intensity. Caution, these values are logarithmic coefficients that stand for very high values (log 1000 = 6.9) (exp 10 # 22000)...(exp 20) # 500.000.000. In theory we should use high "Scale" and gaussian values in the areas closest to the lens (where the veil effect is low) and low values in the distance (where the veil effect is important).

To process the mask issued from the gaussian process

The "Transmission Map" file correspond to a recursive process of:

• The source image – Input image which went through various modifications by the histogram equalizer and the gamma.
• The "mask" files directly issued from the gaussian process (scale, radius, method low.. high…)

"Mask" display type

"Mask" files processing (the idea came to me by studying the Rusell Cottrell's plugin) will allow to decrease flares and artefacts. I added a combo-box to choose the display type. This is both an educational system and also an aid to find the "right" settings.

• Process:
  • Standard: it is the default setting
  • Mask: display the mask obtained by the Retinex algorithm issued from the gaussian process. We will see here the impacts:
    • of the various settings upstream: method (low, uniform, high and highlight), radius, gamma, histogram equalizer. In the other hand, the other settings downstream have no effect on this display: contrast, gain, brightness, threshold and the curve "Transmission Map".
    • of the various settings of the "Mask equalizer".
    • you can export these settings (TIFF/JPG) to use this mask in external software (Gimp, Photoshop).
  • Unsharp mask: you can use this option to subtract the mask to the input image. In this case, an action on "strength" will allow to balance between the input image and the mask. So we get the possibility to have images with very high radius values.
  • Transmission: display an "image" of the "Transmission Map" file.
    • this "image" doesn't match the reality that matches to a logarithmic scale which values are mostly situated between -30 and +30.
    • (fixed)