%%% This is the testing demo for gray image (Gaussian) denoising.

%%% Training data: 400 images of size 180X180

% clear; clc;

addpath('utilities');

folderTest = fullfile('testsets','Set12'); %%% test dataset

%folderTest = 'testsets\BSD68';

folderModel = 'model';

noiseSigma = 25; %%% image noise level

showResult = 1;

useGPU = 1;

pauseTime = 1;

%%% load [specific] Gaussian denoising model

modelSigma = min(75,max(10,round(noiseSigma/5)*5)); %%% model noise level

load(fullfile(folderModel,'specifics',['sigma=',num2str(modelSigma,'%02d'),'.mat']));

%%% load [blind] Gaussian denoising model %%% for sigma in [0,55]

% load(fullfile(folderModel,'GD_Gray_Blind.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));

SSIMs = zeros(1,length(filePaths));

for i = 1:length(filePaths)

%%% read images

label = imread(fullfile(folderTest,filePaths(i).name));

[~,nameCur,extCur] = fileparts(filePaths(i).name);

label = im2double(label);

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 = simplenn_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 and SSIM

[PSNRCur, SSIMCur] = 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',' ',num2str(SSIMCur,'%2.4f')])

drawnow;

pause(pauseTime)

end

PSNRs(i) = PSNRCur;

SSIMs(i) = SSIMCur;

end

disp([mean(PSNRs),mean(SSIMs)]);