diff --git a/Demo_test_DnCNN.m b/Demo_test_DnCNN.m
index 547a8c24..0bb120fa 100644
--- a/Demo_test_DnCNN.m
+++ b/Demo_test_DnCNN.m
@@ -5,12 +5,12 @@
 
 % clear; clc;
 addpath('utilities');
-folderTest  = 'testsets\Set12'; %%% test dataset
-%folderTest  = 'testsets\BSD68';
+testDir = 'testsets';
+folderTest  = 'BSD68'; %%% test dataset
 folderModel = 'model';
 noiseSigma  = 25;  %%% image noise level
 showResult  = 1;
-useGPU      = 1;
+useGPU      = 0; %%% GPU status
 pauseTime   = 1;
 
 %%% load [specific] Gaussian denoising model
@@ -23,8 +23,8 @@
 % load(fullfile(folderModel,'GD_Gray_Blind.mat'));
 
 
-%%%
-% net = vl_simplenn_tidy(net);
+%%% uncomment if necessary for different MatConvNet versions
+%net = vl_simplenn_tidy(net);
 
 % for i = 1:size(net.layers,2)
 %     net.layers{i}.precious = 1;
@@ -36,6 +36,7 @@
 end
 
 %%% read images
+folderTest = fullfile(testDir, folderTest);
 ext         =  {'*.jpg','*.png','*.bmp'};
 filePaths   =  [];
 for i = 1 : length(ext)
diff --git a/Demo_test_DnCNN3.m b/Demo_test_DnCNN3.m
index e13f7508..06b5afd0 100644
--- a/Demo_test_DnCNN3.m
+++ b/Demo_test_DnCNN3.m
@@ -7,7 +7,7 @@
 
 %%% testing set
 tasks       = {'GD','SR','DB'}; %%% three tasks
-imageSets   = {'BSD68','Set5','Set14','BSD100','Urben100','classic5','LIVE1'}; %%% testing dataset
+imageSets   = {'BSD68','Set5','Set14','BSD100','Urban100','classic5','LIVE1'}; %%% testing dataset
 
 %%% setting
 taskTest    = tasks([1 2 3]); %%% choose the tasks for evaluation
@@ -15,7 +15,7 @@
 showResult  = [1 1 1]; %%% save the restored images
 pauseTime   = 1;
 folderModel = 'model';
-useGPU      = 1; % 1 or 0, true or false
+useGPU      = 0; % 1 or 0, true or false
 
 folderTest  = 'testsets';
 folderResult= 'results';
@@ -36,6 +36,7 @@
 %%% load DnCNN-3 model
 load(fullfile(folderModel,'DnCNN3.mat'));
 
+%%% uncomment if necessary for different MatConvNet versions
 %net = vl_simplenn_tidy(net);
 % for i = 1:size(net.layers,2)
 %     net.layers{i}.precious = 1;
diff --git a/Demo_test_DnCNN_SRDN.m b/Demo_test_DnCNN_SRDN.m
new file mode 100644
index 00000000..affce0b1
--- /dev/null
+++ b/Demo_test_DnCNN_SRDN.m
@@ -0,0 +1,341 @@
+
+%%% 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','Set14G','Set14','BSD100','Urben100','classic5','LIVE1'}; %%% testing dataset
+
+%%% setting
+taskTest    = tasks([2]); %%% choose the tasks for evaluation
+setTest     = {imageSets([1]),imageSets([3]),imageSets([6 7])}; %%% select the datasets for each tasks
+showResult  = [1 1 1]; %%% save the restored images
+pauseTime   = 1;
+folderModel = 'model';
+useGPU      = 0; % 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), '_GD']);
+        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');
+            %%% noise
+            randn('seed',0);
+            LR = single(im2double(LR) + sigma/255*randn(size(LR)));
+            
+            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 = im2uint8(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
+
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diff --git a/testsets/hr2grey.m b/testsets/hr2grey.m
new file mode 100644
index 00000000..13304f29
--- /dev/null
+++ b/testsets/hr2grey.m
@@ -0,0 +1,27 @@
+setTestCur = 'Set14';
+ext                 =  {'*.jpg','*.png','*.bmp'};
+filepaths           =  [];
+for i = 1 : length(ext)
+    filepaths = cat(1,filepaths,dir(fullfile(setTestCur, ext{i})));
+end
+%%% folder to store results
+folderResultCur = [setTestCur,'G'];
+if ~exist(folderResultCur,'file')
+    mkdir(folderResultCur);
+end
+for i = 1 : length(filepaths)
+
+    color  = imread(fullfile(setTestCur,filepaths(i).name));
+    [~,imageName,ext] = fileparts(filepaths(i).name);
+    channel = size(color,3);
+
+    if channel == 3
+        %%% label (single)
+        HR_ycc = single(rgb2ycbcr(im2double(color)));
+        label  = HR_ycc(:,:,1);
+        output_RGB = im2uint8(label);
+    else
+        output_RGB = im2uint8(color);
+    end
+    imwrite(output_RGB,fullfile(folderResultCur,[imageName,'.bmp']));
+end