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Author: cszn

Demo_test_CDnCNN_Specific.m

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+% This is the testing demo of CDnCNN for denoising noisy color images corrupted by
+% AWGN.
+
+% clear; clc;
+addpath('utilities');
+folderTest  = fullfile('testsets','CBSD68'); %%% test dataset
+folderModel = 'model';
+
+showResult  = 1;
+useGPU      = 1;
+pauseTime   = 0;
+
+% image noise level
+noiseSigma  = 25;  
+% model noise level
+modelSigma  = 25; % from {5, 10, 15, 25, 35, 50}
+load(fullfile(folderModel,'specifics_color',['color_sigma=',num2str(modelSigma,'%02d'),'.mat']));
+
+net = vl_simplenn_tidy(net);
+
+% for i = 1:size(net.layers,2)
+%     net.layers{i}.precious = 1;
+% end
+
+% move to gpu
+if useGPU
+    net = vl_simplenn_move(net, 'gpu') ;
+end
+
+% read images
+ext         =  {'*.jpg','*.png','*.bmp'};
+filePaths   =  [];
+for i = 1 : length(ext)
+    filePaths = cat(1,filePaths, dir(fullfile(folderTest,ext{i})));
+end
+
+%%% PSNR and SSIM
+PSNRs = zeros(1,length(filePaths));
+
+for i = 1:length(filePaths)
+    
+    % read current image
+    label = imread(fullfile(folderTest,filePaths(i).name));
+    [~,nameCur,extCur] = fileparts(filePaths(i).name);
+    label = im2double(label);
+    
+    % add Gaussian noise
+    randn('seed',0);
+    input = single(label + noiseSigma/255*randn(size(label)));
+    
+    % convert to GPU
+    if useGPU
+        input = gpuArray(input);
+    end
+    
+    res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test');
+    %res = vl_ffdnet_matlab(net, input); %%% use this if you did not install matconvnet.
+    output = input - res(end).x;
+    
+    % convert to CPU
+    if useGPU
+        output = gather(output);
+        input  = gather(input);
+    end
+    
+    % calculate PSNR
+    [PSNRCur] = Cal_PSNRSSIM(im2uint8(label),im2uint8(output),0,0);
+    if showResult
+        imshow(cat(2,im2uint8(label),im2uint8(input),im2uint8(output)));
+        title([filePaths(i).name,'    ',num2str(PSNRCur,'%2.2f'),'dB'])
+        drawnow;
+        pause(pauseTime)
+    end
+    PSNRs(i) = PSNRCur;
+    
+end
+
+disp(mean(PSNRs));
+
+