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Demo_degradation_direct_SRMD.m
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Demo_degradation_direct_SRMD.m
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%==========================================================================
% This is the testing code of SRMDNF for the widely-used degradation with 7x7
% Gaussian kernel of width 1.6 and direct downsampler of scale factor 3.
% For this degradation, the basic setting is:
% 1. the image blur kernel is; "kernel = fspecial('gaussian',7,1.6)".
% 2. the noise level is zero; "nlevel = 0".
% 3. the downsampler is direct downsampler. The scale factor is fixed to 3.
% You can either train a new model with direct downsamper or use the estimated
% kernel k_b under direct downsampler.
% 4. there are three SRMD models, "SRMDx2.mat" for scale factor 2, "SRMDx3.mat"
% for scale factor 3, and "SRMDx4.mat" for scale factor 4.
%==========================================================================
% The basic idea of SRMD is to learn a CNN to infer the MAP of general SISR, i.e.,
% solve x^ = arg min_x 1/(2 sigma^2) ||(kx)\downarrow_s - y||^2 + lamda \Phi(x)
% via x^ = CNN(y,k,sigma;\Theta) or HR^ = CNN(LR,kernel,noiselevel;\Theta).
%
% There involves two important factors, i.e., blur kernel (k; kernel) and noise
% level (sigma; nlevel).
%
% For more information, please refer to the following paper.
% @article{zhang2017learningsrmd,
% title={Learning a Single Convolutional Super-Resolution Network for Multiple Degradations},
% author={Kai, Zhang and Wangmeng, Zuo and Lei, Zhang},
% year={2017},
% }
%
% If you have any question, please feel free to contact with <Kai Zhang ([email protected])>.
%
% This code is for research purpose only.
%
% by Kai Zhang (Nov, 2017)
%==========================================================================
% clear; clc;
format compact;
addpath('utilities');
imageSets = {'Set5','Set14','BSD100','Urban100'}; % testing dataset
%% select testing dataset, use GPU or not, ...
setTest = imageSets([1,2,3,4]); % select the datasets for each tasks
showResult = 0; % save restored images
pauseTime = 0;
useGPU = 1; % 1 or 0, true or false
method = 'SRMD';
folderTest = 'testsets';
folderResult = 'results';
if ~exist(folderResult,'file')
mkdir(folderResult);
end
%% scale factor (3 only)
sf = 3; % Here, do not set sf to 2 or 4.
%% load model with scale factor sf
folderModel = 'models';
load(fullfile(folderModel,['SRMDx',int2str(sf),'.mat']));
%net.layers = net.layers(1:end-1);
net = vl_simplenn_tidy(net);
if useGPU
net = vl_simplenn_move(net, 'gpu') ;
end
%% degradation parameter (kernel & noise level) setting
global degpar;
image_kernel = fspecial('gaussian',7, 1.6); % 7x7 Gaussian kernel k_d with width 1.6
nlevel = 0; % noise level is zero.
kernel = net.meta.directKernel(:,:,:,5); % corrresponding k_b under bicubic downsampler
degpar = single([net.meta.P*kernel(:); nlevel(:)/255]);
tag = ['_',method,'_x',num2str(sf),'_directG_7_16'];
for n_set = 1 : numel(setTest)
%% search 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
%% prepare results
eval(['PSNR_',setTestCur,'_x',num2str(sf),' = zeros(length(filepaths),1);']);
eval(['SSIM_',setTestCur,'_x',num2str(sf),' = zeros(length(filepaths),1);']);
folderResultCur = fullfile(folderResult, [setTestCur,tag]);
if ~exist(folderResultCur,'file')
mkdir(folderResultCur);
end
%% perform SISR
for i = 1 : length(filepaths)
HR = imread(fullfile(folderTestCur,filepaths(i).name));
C = size(HR,3);
if C == 1
HR = cat(3,HR,HR,HR);
end
[~,imageName,ext] = fileparts(filepaths(i).name);
HR = modcrop(HR, sf);
label_RGB = HR;
%% bicubic degradation
blury_HR = imfilter(im2double(HR),double(image_kernel),'replicate'); % blur
LR = imresize(im2double(blury_HR),1/sf,'nearest'); % bicubic downsampling
randn('seed',0);
LR_noisy = LR + nlevel/255.*randn(size(LR)); % add random noise (AWGN)
input = single(LR_noisy);
%input = im2single(im2uint8(LR)); % another widely-used setting
%tic
if useGPU
input = gpuArray(input);
end
res = vl_srmd(net, input,[],[],'conserveMemory',true,'mode','test','cudnn',true);
%res = vl_srmd_concise(net, input); % a concise version of "vl_srmd".
%res = vl_srmd_matlab(net, input); % you should also set "useGPU = 0;" and comment "net = vl_simplenn_tidy(net);"
output_RGB = gather(res(end).x);
%toc;
if C == 1
label = mean(im2double(HR),3);
output = mean(output_RGB,3);
else
label = rgb2ycbcr(im2double(HR));
output = rgb2ycbcr(double(output_RGB));
label = label(:,:,1);
output = output(:,:,1);
end
%% calculate PSNR and SSIM
[PSNR_Cur,SSIM_Cur] = Cal_PSNRSSIM(label*255,output*255,sf,sf); %%% single
disp([setTestCur,' ',int2str(i),' ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_',setTestCur,'_x',num2str(sf),'(',num2str(i),') = PSNR_Cur;']);
eval(['SSIM_',setTestCur,'_x',num2str(sf),'(',num2str(i),') = SSIM_Cur;']);
if showResult
imshow(cat(2,label_RGB,imresize(im2uint8(LR),sf),im2uint8(output_RGB)));
drawnow;
title(['SISR ',filepaths(i).name,' ',num2str(PSNR_Cur,'%2.2f'),'dB'],'FontSize',12)
pause(pauseTime)
imwrite(output_RGB,fullfile(folderResultCur,[imageName,'_x',int2str(sf),'_',int2str(PSNR_Cur*100),'.png']));% save results
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',setTestCur,'_x',num2str(sf)])),'%2.2f'),'dB']);
disp(['Average SSIM is ',num2str(mean(eval(['SSIM_',setTestCur,'_x',num2str(sf)])),'%2.4f')]);
%% save PSNR and SSIM results
save(fullfile(folderResultCur,['PSNR_',setTestCur,'_x',num2str(sf),'.mat']),['PSNR_',setTestCur,'_x',num2str(sf)]);
save(fullfile(folderResultCur,['SSIM_',setTestCur,'_x',num2str(sf),'.mat']),['SSIM_',setTestCur,'_x',num2str(sf)]);
end