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Demo_SISR_YCbCr.m
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Demo_SISR_YCbCr.m
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% Single Image Super-Resolution (SISR)
% @inproceedings{zhang2017learning,
% title={Learning Deep CNN Denoiser Prior for Image Restoration},
% author={Zhang, Kai and Zuo, Wangmeng and Gu, Shuhang and Zhang, Lei},
% booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
% year={2017}
% }
% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: cskaizhang@gmail.com)
% clear; clc;
addpath('utilities');
imageSets = {'Set5','Set14'}; %%% testing dataset
%%% setting
setTest = imageSets([1]); %%% select the dataset
showResult = 1;
pauseTime = 1;
useGPU = 1; % 1 or 0, true or false
folderTest = 'testsets';
folderResult= 'results';
taskTestCur = 'SISR';
if ~exist(folderResult,'file')
mkdir(folderResult);
end
kernelTypes = {'bicubic','Gaussian'};
kernelType_image = kernelTypes{1};
kernelType_model = kernelTypes{1};
scaleFactor = 3;
totalIter = 30;
inIter = 5;
alpha = 1.75;
kernelsigma = 1.6; % ****** from [0.6 2.4] ******
modelSigmaS = logspace(log10(12*scaleFactor),log10(scaleFactor),totalIter);
ns = min(25,max(ceil(modelSigmaS/2),1));
ns = [ns(1)-1,ns];
folderModel = 'models';
load(fullfile(folderModel,'modelgray.mat'));
for n_set = 1 : numel(setTest)
%%% read images
setTestCur = cell2mat(setTest(n_set));
disp('--------------------------------------------');
disp(['----',setTestCur,'-----Super-Resolution-----']);
disp('--------------------------------------------');
folderTestCur = fullfile(folderTest,setTestCur);
ext = {'*.jpg','*.png','*.bmp'};
filepaths = [];
for i = 1 : length(ext)
filepaths = cat(1,filepaths,dir(fullfile(folderTestCur, ext{i})));
end
eval(['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor),' = zeros(length(filepaths),1);']);
eval(['PSNRC_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor),' = zeros(length(filepaths),1);']);
%%% folder to store results
folderResultCur = fullfile(folderResult, ['SISR_YCbCr_',setTestCur,'_x',num2str(scaleFactor),'_',kernelType_image,'_',kernelType_model]);
if ~exist(folderResultCur,'file')
mkdir(folderResultCur);
end
for i = 1 : length(filepaths)
HR = imread(fullfile(folderTestCur,filepaths(i).name));
[~,imageName,ext] = fileparts(filepaths(i).name);
HR = modcrop(HR, scaleFactor);
%%% label_RGB (uint8)
label_RGB = HR;
chanel = size(HR,3);
%%% LR (uint8)
LR = imresize_down(HR,scaleFactor,kernelType_image,kernelsigma);
LR = uint8(LR);
if chanel == 3
%%% label (single)
HR_ycc = single(rgb2ycbcr(im2double(HR)));
label = HR_ycc(:,:,1);
LR_ycc = single(rgb2ycbcr(im2double(LR)));
LRY = LR_ycc(:,:,1);
%%% input (single)
HR_bic = imresize(im2double(LR),scaleFactor,'bicubic');
LR_bic_ycc = rgb2ycbcr(HR_bic);
input = im2single(LR_bic_ycc(:,:,1));
%%% input_RGB (uint8)
input_RGB = im2uint8(HR_bic);
else
%%% label (single)
label = im2single(HR);
LRY = im2single(LR);
HR_bic = imresize(LR,scaleFactor,'bicubic');
%%% input (single)
input = im2single(HR_bic);
%%% input_RGB (uint8)
input_RGB = HR_bic;
end
if useGPU
input = gpuArray(input);
LRY = gpuArray(LRY);
end
output = input;
tic;
for itern = 1:totalIter
%%% step 1
for k = 1:inIter
output = output + alpha*imresize((LRY - imresize_down(output,scaleFactor,kernelType_model,kernelsigma)),scaleFactor,'bicubic');
end
if ns(itern+1)~=ns(itern)
[net] = loadmodel(modelSigmaS(itern),CNNdenoiser);
net = vl_simplenn_tidy(net);
if useGPU
net = vl_simplenn_move(net, 'gpu');
end
end
%%% step 2
res = vl_simplenn(net, output,[],[],'conserveMemory',true,'mode','test');
im = res(end).x;
output = output - im;
end
if useGPU
output = gather(output);
end
toc;
if chanel == 3
%%% output_RGB (uint8)
LR_bic_ycc(:,:,1) = double(output);
output_RGB = im2uint8(ycbcr2rgb(LR_bic_ycc));
else
%%% output_RGB (uint8)
output_RGB = im2uint8(output);
end
[PSNR_Cur,SSIM_Cur] = Cal_PSNRSSIM(label*255,output*255,ceil(scaleFactor),ceil(scaleFactor)); %%% single
[PSNRC_Cur,SSIM_Cur_RGB] = Cal_PSNRSSIM(label_RGB,output_RGB,ceil(scaleFactor),ceil(scaleFactor)); %%% single
disp(['Single Image Super-Resolution ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor),'(',num2str(i),') = PSNR_Cur;']);
eval(['PSNRC_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor),'(',num2str(i),') = PSNRC_Cur;']);
if showResult
imshow(cat(1,cat(2,input_RGB,output_RGB),cat(2,(output_RGB-input_RGB),label_RGB)));
drawnow;
title(['Single Image Super-Resolution ',filepaths(i).name,' ',num2str(PSNR_Cur,'%2.2f'),'dB'],'FontSize',12)
pause(pauseTime)
%pause()
imwrite(output_RGB,fullfile(folderResultCur,[imageName,'_x',num2str(scaleFactor),'.png']));
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor)])),'%2.2f'),'dB']);
disp(['Average PSNRC is ',num2str(mean(eval(['PSNRC_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor)])),'%2.4f')]);
%%% save PSNR and SSIM metrics
save(fullfile(folderResultCur,['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor),'.mat']),['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor)])
save(fullfile(folderResultCur,['PSNRC_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor),'.mat']),['PSNRC_',taskTestCur,'_',setTestCur,'_x',num2str(scaleFactor)])
end