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Demo_TWSC_RGB2YUV_CC2016.m
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Demo_TWSC_RGB2YUV_CC2016.m
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%-------------------------------------------------------------------------------------------------------------
% This is an implementation of the TWSC algorithm for real-world image denoising
% by processing the YUV channels insead of RGB channels, it would improve
% TWSC by at 0.1dB on PSNR.
% Author: Jun Xu, csjunxu@comp.polyu.edu.hk / nankaimathxujun@gmail.com
% The Hong Kong Polytechnic University
%
% Please refer to the following paper if you find this code helps:
%
% @article{TWSC_ECCV2018,
% author = {Jun Xu and Lei Zhang and David Zhang},
% title = {A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising},
% journal = {ECCV},
% year = {2018}
% }
%
% Please see the file License.txt for the license governing this code.
%-------------------------------------------------------------------------------------------------------------
clear;
GT_Original_image_dir = 'Real_ccnoise_denoised_Part/';
GT_fpath = fullfile(GT_Original_image_dir, '*mean.png');
TT_Original_image_dir = 'Real_ccnoise_denoised_Part/';
TT_fpath = fullfile(TT_Original_image_dir, '*real.png');
GT_im_dir = dir(GT_fpath);
TT_im_dir = dir(TT_fpath);
im_num = length(TT_im_dir);
method = 'TWSC_rgb2yuv';
dataset = 'cc';
write_MAT_dir = [dataset '_Results/'];
write_sRGB_dir = [write_MAT_dir method];
if ~isdir(write_sRGB_dir)
mkdir(write_sRGB_dir)
end
% Parameters
Par.ps = 6; % patch size
Par.step = 3; % the step of two neighbor patches
Par.win = 20; % size of window around the patch
Par.Outerloop = 7;
Par.Innerloop = 2;
Par.nlspini = 70;
Par.display = 0;
Par.delta = 0;
Par.nlspgap = 0;
Par.lambda1 = 0;
Par.lambda2 = 4.9;
Par.nlspini = 70;
% record all the results in each iteration
Par.PSNR = zeros(Par.Outerloop, im_num, 'double');
Par.SSIM = zeros(Par.Outerloop, im_num, 'double');
for i = 1 : im_num
Par.nlsp = Par.nlspini; % number of non-local patches
Par.image = i;
Par.nim = im2double(imread(fullfile(TT_Original_image_dir, TT_im_dir(i).name) ));
Par.I = im2double(imread(fullfile(GT_Original_image_dir, GT_im_dir(i).name)));
S = regexp(TT_im_dir(i).name, '\.', 'split');
IMname = S{1};
% initial PSNR and SSIM
fprintf('%s: \n', TT_im_dir(i).name);
fprintf('The initial PSNR = %2.4f, SSIM = %2.4f. \n', csnr( Par.nim*255, Par.I*255, 0, 0 ), cal_ssim( Par.nim*255, Par.I*255, 0, 0 ));
Par.nim = rgb2ycbcr(Par.nim);
[h,w,ch] = size(Par.nim);
% noise estimation
for c = 1:ch
Par.nSig(c) = NoiseEstimation(Par.nim(:, :, c)*255, Par.ps)/255;
end
[IMout, Par] = TWSC_Sigma_RW(Par);
IMout = ycbcr2rgb(IMout);
% calculate the PSNR
Par.PSNR(Par.Outerloop, Par.image) = csnr( IMout*255, Par.I*255, 0, 0 );
Par.SSIM(Par.Outerloop, Par.image) = cal_ssim( IMout*255, Par.I*255, 0, 0 );
%% output
imwrite(IMout, [write_sRGB_dir '/' method '_rgb2yuv_' dataset '_' IMname '.png']);
fprintf('%s : PSNR = %2.4f, SSIM = %2.4f \n',TT_im_dir(i).name, Par.PSNR(Par.Outerloop, Par.image),Par.SSIM(Par.Outerloop, Par.image) );
end
PSNR =Par.PSNR(end,:);
SSIM = Par.SSIM(end,:);
mPSNR=mean(PSNR,2);
mSSIM=mean(SSIM,2);
matname = sprintf([write_MAT_dir method '_rgb2yuv_' dataset '.mat']);
save(matname,'PSNR','SSIM','mPSNR','mSSIM');