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Demo_test_DnCNN3.m
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Demo_test_DnCNN3.m
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%%% This is the testing demo for learning a single model for three tasks, including Gaussian denoing, SISR, JPEG image deblocking.
% clear; clc;
addpath('utilities');
%%% testing set
tasks = {'GD','SR','DB'}; %%% three tasks
imageSets = {'BSD68','Set5','Set14','BSD100','Urben100','classic5','LIVE1'}; %%% testing dataset
%%% setting
taskTest = tasks([1 2 3]); %%% choose the tasks for evaluation
setTest = {imageSets([1]),imageSets([2:5]),imageSets([6 7])}; %%% select the datasets for each tasks
showResult = [1 1 1]; %%% save the restored images
pauseTime = 1;
folderModel = 'model';
useGPU = 1; % 1 or 0, true or false
folderTest = 'testsets';
folderResult= 'results';
if ~exist(folderResult,'file')
mkdir(folderResult);
end
%%% task GD = Gaussian Denoising
sigma = 25;
%%% task SR = Single Image Super-Resolution
scale = 3;
%%% task DB = DeBlocking
Q = 20;
%%% load DnCNN-3 model
load(fullfile(folderModel,'DnCNN3.mat'));
%net = vl_simplenn_tidy(net);
% for i = 1:size(net.layers,2)
% net.layers{i}.precious = 1;
% end
if useGPU
net = vl_simplenn_move(net, 'gpu') ;
end
%%% input (single); output (single); label (ground-truth, uint8)
%%% input_RGB (uint8); output_RGB (uint8); label_RGB (ground-truth, uint8)
%%%-------------------------------------------------------------------------------------
%%% Gaussian Denoising (GD)
%%%-------------------------------------------------------------------------------------
if ismember('GD',taskTest)
taskTestCur = 'GD';
for n_set = 1 : numel(setTest{1})
%%% read images
setTestCur = cell2mat(setTest{1}(n_set));
disp('-----------------------------------------------');
disp(['----',setTestCur,'------Gaussian Denoising-----']);
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,'_s',num2str(sigma),' = zeros(length(filepaths),1);']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),' = zeros(length(filepaths),1);']);
%%% folder to store results
folderResultCur = fullfile(folderResult, [taskTestCur,'_',setTestCur,'_s',num2str(sigma)]);
if ~exist(folderResultCur,'file')
mkdir(folderResultCur);
end
for i = 1 : length(filepaths)
label = imread(fullfile(folderTestCur,filepaths(i).name));
[~,imageName,ext] = fileparts(filepaths(i).name);
chanel = size(label,3);
if chanel == 3
%%% label (uint8)
label = rgb2gray(label);
end
%%% input (single)
randn('seed',0);
input = single(im2double(label) + sigma/255*randn(size(label)));
if useGPU
input = gpuArray(input);
end
res = vl_simplenn(net, input,[],[],'conserveMemory',true,'mode','test');
im = res(end).x;
%%% output (single)
output = gather(input - im);
[PSNR_Cur,SSIM_Cur] = Cal_PSNRSSIM(label,im2uint8(output),0,0);
disp(['Denoising ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'(',num2str(i),') = PSNR_Cur;']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'(',num2str(i),') = SSIM_Cur;']);
if showResult(1)
imshow(cat(1,cat(2,im2uint8(input),im2uint8(output)),cat(2,im2uint8(abs(input-output)*10),label)));
drawnow;
title(['Denoising ',filepaths(i).name,' ',num2str(PSNR_Cur,'%2.2f'),'dB'],'FontSize',12)
pause(pauseTime)
%pause()
%%% save results
imwrite(output,fullfile(folderResultCur,[imageName,'_s',num2str(sigma),'.png']));
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])),'%2.2f'),'dB']);
disp(['Average SSIM is ',num2str(mean(eval(['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])),'%2.4f')]);
%%% save PSNR and SSIM metrics
save(fullfile(folderResultCur,['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'.mat']),['PSNR_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])
save(fullfile(folderResultCur,['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma),'.mat']),['SSIM_',taskTestCur,'_',setTestCur,'_s',num2str(sigma)])
end
end
%%%-------------------------------------------------------------------------------------
%%% Single Image Super-Resolution (SR)
%%%-------------------------------------------------------------------------------------
if ismember('SR',taskTest)
taskTestCur = 'SR';
for n_set = 1 : numel(setTest{2})
%%% read images
setTestCur = cell2mat(setTest{2}(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(scale),' = zeros(length(filepaths),1);']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale),' = zeros(length(filepaths),1);']);
if fix(scale) == scale
crop = scale;
else
crop = scale*10;
end
%%% folder to store results
folderResultCur = fullfile(folderResult, [taskTestCur,'_',setTestCur,'_x',num2str(scale)]);
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, crop);
%%% label_RGB (uint8)
label_RGB = HR;
chanel = size(HR,3);
%%% LR (uint8)
LR = imresize(HR,1/scale,'bicubic');
if chanel == 3
%%% label (single)
HR_ycc = single(rgb2ycbcr(im2double(HR)));
label = HR_ycc(:,:,1);
%%% input (single)
HR_bic = imresize(im2double(LR),scale,'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);
HR_bic = imresize(LR,scale,'bicubic');
%%% input (single)
input = im2single(HR_bic);
%%% input_RGB (uint8)
input_RGB = HR_bic;
end
if useGPU
input = gpuArray(input);
end
res = vl_simplenn(net, input,[],[],'conserveMemory',true,'mode','test');
im = res(end).x;
%%% output (single)
output = gather(input - im);
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(scale),ceil(scale)); %%% single
disp(['Single Image Super-Resolution ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_SR_',setTestCur,'_x',num2str(scale),'(',num2str(i),') = PSNR_Cur;']);
eval(['SSIM_SR_',setTestCur,'_x',num2str(scale),'(',num2str(i),') = SSIM_Cur;']);
if showResult(2)
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()
%%% save results
imwrite(output_RGB,fullfile(folderResultCur,[imageName,'_x',num2str(scale),'.png']));
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])),'%2.2f'),'dB']);
disp(['Average SSIM is ',num2str(mean(eval(['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])),'%2.4f')]);
%%% save PSNR and SSIM metrics
save(fullfile(folderResultCur,['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scale),'.mat']),['PSNR_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])
save(fullfile(folderResultCur,['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale),'.mat']),['SSIM_',taskTestCur,'_',setTestCur,'_x',num2str(scale)])
end
end
%%%-------------------------------------------------------------------------------------
%%% JPEG Image Deblocking (DB)
%%%-------------------------------------------------------------------------------------
if ismember('DB',taskTest)
taskTestCur = 'DB';
for n_set = 1 : numel(setTest{3})
%%% read image names
setTestCur = cell2mat(setTest{3}(n_set));
disp('---------------------------------------');
disp(['----',setTestCur,'------Deblocking-----']);
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
%%% to store PSNR and SSIM results
eval(['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q),' = zeros(length(filepaths),1);']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q),' = zeros(length(filepaths),1);']);
%%% to store results
folderResultCur = fullfile(folderResult, [taskTestCur,'_',setTestCur,'_q',num2str(Q)]);
if ~exist(folderResultCur,'file')
mkdir(folderResultCur);
end
for i = 1 : length(filepaths)
label = imread(fullfile(folderTestCur,filepaths(i).name));
[~,imageName,ext] = fileparts(filepaths(i).name);
chanel = size(label,3);
if chanel == 3
%%% label (uint8)
label = rgb2ycbcr(label);
label = label(:,:,1);
end
%%% input (single)
imwrite(label,'test.jpg','jpg','quality',Q);
input = im2single(imread('test.jpg'));
if useGPU
input = gpuArray(input);
end
res = vl_simplenn(net, input,[],[],'conserveMemory',true,'mode','test');
im = res(end).x;
%%% output (single)
output = gather(input - im);
[PSNR_Cur,SSIM_Cur] = Cal_PSNRSSIM(label,im2uint8(output),0,0);
disp(['Deblocking ',num2str(PSNR_Cur,'%2.2f'),'dB',' ',filepaths(i).name]);
eval(['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'(',num2str(i),') = PSNR_Cur;']);
eval(['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'(',num2str(i),') = SSIM_Cur;']);
if showResult(3)
imshow(cat(1,cat(2,im2uint8(input),im2uint8(output)),cat(2,im2uint8(abs(input-output)*10),label)));
drawnow;
title(['Deblocking ',filepaths(i).name,' ',num2str(PSNR_Cur,'%2.2f'),'dB'],'FontSize',12)
pause(pauseTime)
%%% save results
imwrite(output,fullfile(folderResultCur,[imageName,'_q',num2str(Q),'.png']));
end
end
disp(['Average PSNR is ',num2str(mean(eval(['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])),'%2.2f'),'dB']);
disp(['Average SSIM is ',num2str(mean(eval(['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])),'%2.4f')]);
%%% save PSNR and SSIM metrics
save(fullfile(folderResultCur,['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'.mat']),['PSNR_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])
save(fullfile(folderResultCur,['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q),'.mat']),['SSIM_',taskTestCur,'_',setTestCur,'_q',num2str(Q)])
end
end