% LOGCOLOURNORMALIZATION - Chromaticity, grey, or comprehensive colour normalization.
%
% Useage: [imgcn, imgn] = logcolournormalization(img, option, scale)
%
% Arguments:
% img - Image to be normalized
% option - String 'comprehensive', 'chromaticity' or 'grey'/'gray', only
% the first 4 characters need be specified.
% Default is 'comprehensive'
% - 'chromaticity' performs RGB normalization on each pixel.
% r = R/mean(R,G,B) etc
% - 'grey' performs a 'grey world' normalization where r =
% R/mean(R) etc. where the mean is taken over the red component
% of all pixel in the image. The result is supposedly
% independent of illumination colour, however the result
% depends on image content because the mean red, green and blue
% values are made equal.
% - 'comprehensive' implements Finlayson and Xu's non-iterative
% comprehensive normalization which simultaneously normalizes
% chromaticity and illumination colour.
% scale - Optional scaling (dividing) factor. Defaults to 3. Chromaticity
% and grey normalization result in image values having a mean
% close to 1. Dividing by 3 makes the result comparable to simple
% colour normalization where r = R/(R+B+G) where the expected value
% is around 1/3.
% Returns:
% imgcn - Colour normalized image of type double with values in the range 0-1.
% imgn - Raw image rescaled to have a maximum value of 1.
%
% Reference:
% Graham Finlayson and Ruixia Xu
% Non-iterative Comprehensive Normalization
% Proc. CGIV 2002
% Copyright (c) 2017 Peter Kovesi
% Centre for Exploration Targeting
% School of Earth Sciences
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% PK August 2017
function [imgcn, imgn] = logcolournormalization(img, option, scale)
if ~exist('option', 'var'), option = 'comprehensive'; end
if ~exist('scale', 'var'), scale = 3; end
if ~(strncmpi(option, 'comprehensive', 4) || ...
strncmpi(option, 'chromaticity',4) || ...
strncmpi(option, 'grey',4)) || ...
strncmpi(option, 'gray',4))
error('Unrecognized option');
end
% Normalize image to 0-1 and ignore alpha channel if it exists.
if isa(img, 'uint8')
imgn = double(img(:,:,1:3))/255;
elseif isa(img, 'uint16')
imgn = double(img(:,:,1:3))/2^16;
elseif isa(img, 'double')
imgn = img./max(img(:));
end
zeroOffset = 0.01; % Avoid log of 0
imgcn = log(imgn + zeroOffset); % Work in the log domain
% Chromaticity normalization step. This gives invariance to lighting
% geometry and illumination magnitude.
if strncmpi(option, 'chromaticity',4) || ...
strncmpi(option, 'comprehensive', 4)
meanv = mean(imgcn,3);
for ch = 1:3
imgcn(:,:,ch) = imgcn(:,:,ch) - meanv;
end
end
% Grey normalization step (grey world). This gives invariance to
% illumination colour (but the result depends on the image content).
if strncmpi(option, 'grey',4) || ...
strncmpi(option, 'gray',4) || ...
strncmpi(option, 'comprehensive', 4)
for ch = 1:3
tmp = imgcn(:,:,ch);
meanch = mean(tmp(:));
% meanch = median(tmp(:)); % Median does not make much of a difference.
imgcn(:,:,ch) = imgcn(:,:,ch) - meanch;
end
end
% Re-exponentiate and divide by three so that normalization is roughly
% equivalent to dividing by (R+G+B) as per normal colour normalization
imgcn = (exp(imgcn)-zeroOffset)/scale;
% Clip any values above 1 (on some sensors one can get large green values)
imgcn(imgcn > 1) = 1;