Demo_REAL_Color.m

% This is the testing demo of FFDNet for denoising real noisy (color) images.
%
% To run the code, you should install Matconvnet first. Alternatively, you can use the
% function `vl_ffdnet_matlab` to perform denoising without Matconvnet.
%
% "FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising"
% 2018/03/23
% If you have any question, please feel free to contact with me.
% Kai Zhang (e-mail: cskaizhang@gmail.com)

% clear; clc;
format compact;
global sigmas; % input noise level or input noise level map
addpath(fullfile('utilities'));

folderModel = 'models';
folderTest  = 'testsets';
folderResult= 'results';
imageSets   = {'RNI15'}; % testing datasets
setTestCur  = imageSets{1}; % current testing dataset

showResult  = 1;
useGPU      = 0;
pauseTime   = 0;

inputNoiseSigma = 15;  % input noise level (Sigma_RGB)

% -****************************************************-
% Audrey_Hepburn.jpg (inputNoiseSigma = 10); i = 1
% Bears.png          (inputNoiseSigma = 15); i = 2
% Boy.png            (inputNoiseSigma = 45); i = 3
% Dog.png            (inputNoiseSigma = 28); i = 4
% Flowers.png        (inputNoiseSigma = 70); i = 5
% Frog.png           (inputNoiseSigma = 15); i = 6
% Movie.png          (inputNoiseSigma = 12); i = 8
% Pattern1.png       (inputNoiseSigma = 12); i = 9
% Pattern2.png       (inputNoiseSigma = 40); i = 10
% Pattern3.png       (inputNoiseSigma = 25); i = 11
% Postcards.png      (inputNoiseSigma = 15); i = 12
% Singer.png         (inputNoiseSigma = 30); i = 13
% Stars.png          (inputNoiseSigma = 18); i = 14
% Window.png         (inputNoiseSigma = 15); i = 15
% -****************************************************-

folderResultCur       =  fullfile(folderResult, [setTestCur,'_',num2str(inputNoiseSigma(1))]);
if ~isdir(folderResultCur)
    mkdir(folderResultCur)
end

load(fullfile('models','FFDNet_color.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

% read images
ext         =  {'*.jpg','*.png','*.bmp','*.tif'};
filePaths   =  [];
for i = 1 : length(ext)
    filePaths = cat(1,filePaths, dir(fullfile(folderTest,setTestCur,ext{i})));
end


for i = 6
    
    %% read images
    disp([filePaths(i).name])
    label = imread(fullfile(folderTest,setTestCur,filePaths(i).name));
    [w,h,c]=size(label);
    
    if c == 3
        [~,nameCur,extCur] = fileparts(filePaths(i).name);
        
        input = im2single(label);
        
        if mod(w,2)==1
            input = cat(1,input, input(end,:,:)) ;
        end
        if mod(h,2)==1
            input = cat(2,input, input(:,end,:)) ;
        end
        
        if useGPU
            input = gpuArray(input);
        end
        
        % set model noise level map
        sigmas = inputNoiseSigma/255; % see "vl_simplenn.m".
        
        % perform denoising
        res    = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test'); % matconvnet default
        % res    = vl_ffdnet_concise(net, input);    % concise version of vl_simplenn for testing FFDNet
        % res    = vl_ffdnet_matlab(net, input); % use this if you did  not install matconvnet; very slow
        
        % output = input -res(end).x; % for 'model_color.mat'
        output = res(end).x;
        
        if mod(w,2)==1
            output = output(1:end-1,:,:);
            input  = input(1:end-1,:,:);
        end
        if mod(h,2)==1
            output = output(:,1:end-1,:);
            input  = input(:,1:end-1,:);
        end
        
        if useGPU
            output = gather(output);
            input  = gather(input);
        end
        
        if showResult
            imshow(cat(2,im2uint8(input),im2uint8(output)));
            title([filePaths(i).name])
            imwrite(im2uint8(output), fullfile(folderResultCur, [nameCur,'_' num2str(inputNoiseSigma(1),'%02d'), '.png'] ));
            drawnow;
            pause(pauseTime)
        end
        
        
    end
end