Evaluate_PSNR_SSIM.m

function Evaluate_PSNR_SSIM()
% %==========================================================================
% % This script produces the PSNR and SSIM results with BI, BD, and Dn modeld for our CVPR18 paper (Tables 2, 3)
% % The training and test codes would be released soon.
% % @inproceedings{zhang2018residual,
% %   title={Residual Dense Network for Image Super-Resolution},
% %   author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},
% %   booktitle={CVPR},
% %   year={2018}
% % }
% % by Yulun Zhang, yulun100@gmail.com
% % 2018/02/25
% %==========================================================================
clear all; close all; clc

%% set path
degradation = 'DN'; % BI, BD, DN
methods = {'RDN', 'RDNplus'};
dataset = {'Set5'};
ext = {'*.jpg', '*.png', '*.bmp'};
num_method = length(methods);
num_set = length(dataset);
record_results_txt = ['PSNR_SSIM_Results_', degradation,'_model.txt'];
results = fopen(fullfile(record_results_txt), 'wt');

if strcmp(degradation, 'BI') 
    scale_all = 2:4;
else
    scale_all = 3;
end

for idx_method = 1:num_method
    
    for idx_set = 1:num_set
        fprintf(results, '**********************\n');
        fprintf(results, 'Method_%d: %s; Set: %s\n', idx_method, methods{idx_method}, dataset{idx_set});
        fprintf('**********************\n');
        fprintf('Method_%d: %s; Set: %s\n', idx_method, methods{idx_method}, dataset{idx_set});
        for scale = scale_all
            filepaths = [];
            for idx_ext = 1:length(ext)
                filepaths = cat(1, filepaths, dir(fullfile('./HR', dataset{idx_set}, ['x', num2str(scale)], ext{idx_ext})));
            end
            PSNR_all = zeros(1, length(filepaths));
            SSIM_all = zeros(1, length(filepaths));
            for idx_im = 1:length(filepaths)
                name_HR = filepaths(idx_im).name;
                name_SR = strrep(name_HR, 'HR', methods{idx_method});
                im_HR = imread(fullfile('./HR', dataset{idx_set}, ['x', num2str(scale)], name_HR));
                im_SR = imread(fullfile('./SR', degradation, [methods{idx_method}], dataset{idx_set}, ['x', num2str(scale)], name_SR));
                % change channel for evaluation
                if 3 == size(im_HR, 3)
                    im_HR_YCbCr = single(rgb2ycbcr(im2double(im_HR)));
                    im_HR_Y = im_HR_YCbCr(:,:,1);
                    im_SR_YCbCr = single(rgb2ycbcr(im2double(im_SR)));
                    im_SR_Y = im_SR_YCbCr(:,:,1);
                else
                    im_HR_Y = single(im2double(im_HR));
                    im_SR_Y = single(im2double(im_SR));
                end
                % calculate PSNR, SSIM
                [PSNR_all(idx_im), SSIM_all(idx_im)] = Cal_Y_PSNRSSIM(im_HR_Y*255, im_SR_Y*255, scale, scale);
                fprintf(results, 'x%d %d %s: PSNR= %f SSIM= %f\n', scale, idx_im, name_SR, PSNR_all(idx_im), SSIM_all(idx_im));
                fprintf('x%d %d %s: PSNR= %f SSIM= %f\n', scale, idx_im, name_SR, PSNR_all(idx_im), SSIM_all(idx_im));
            end
            fprintf(results, '--------Mean--------\n');
            fprintf('--------Mean--------\n');
            fprintf(results, 'x%d: PSNR= %f SSIM= %f\n', scale, mean(PSNR_all), mean(SSIM_all));
            fprintf('x%d: PSNR= %f SSIM= %f\n', scale, mean(PSNR_all), mean(SSIM_all));
        end
    end
end
fclose(results);

end

function [psnr_cur, ssim_cur] = Cal_Y_PSNRSSIM(A,B,row,col)
% shave border if needed
if nargin > 2
    [n,m,~]=size(A);
    A = A(row+1:n-row,col+1:m-col,:);
    B = B(row+1:n-row,col+1:m-col,:);
end
% RGB --> YCbCr
if 3 == size(A, 3)
    A = rgb2ycbcr(A);
    A = A(:,:,1);
end
if 3 == size(B, 3)
    B = rgb2ycbcr(B);
    B = B(:,:,1);
end
% calculate PSNR
A=double(A); % Ground-truth
B=double(B); %

e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);

% calculate SSIM
[ssim_cur, ~] = ssim_index(A, B);
end


function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L)

%========================================================================
%SSIM Index, Version 1.0
%Copyright(c) 2003 Zhou Wang
%All Rights Reserved.
%
%The author is with Howard Hughes Medical Institute, and Laboratory
%for Computational Vision at Center for Neural Science and Courant
%Institute of Mathematical Sciences, New York University.
%
%----------------------------------------------------------------------
%Permission to use, copy, or modify this software and its documentation
%for educational and research purposes only and without fee is hereby
%granted, provided that this copyright notice and the original authors'
%names appear on all copies and supporting documentation. This program
%shall not be used, rewritten, or adapted as the basis of a commercial
%software or hardware product without first obtaining permission of the
%authors. The authors make no representations about the suitability of
%this software for any purpose. It is provided "as is" without express
%or implied warranty.
%----------------------------------------------------------------------
%
%This is an implementation of the algorithm for calculating the
%Structural SIMilarity (SSIM) index between two images. Please refer
%to the following paper:
%
%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
%quality assessment: From error measurement to structural similarity"
%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
%
%Kindly report any suggestions or corrections to zhouwang@ieee.org
%
%----------------------------------------------------------------------
%
%Input : (1) img1: the first image being compared
%        (2) img2: the second image being compared
%        (3) K: constants in the SSIM index formula (see the above
%            reference). defualt value: K = [0.01 0.03]
%        (4) window: local window for statistics (see the above
%            reference). default widnow is Gaussian given by
%            window = fspecial('gaussian', 11, 1.5);
%        (5) L: dynamic range of the images. default: L = 255
%
%Output: (1) mssim: the mean SSIM index value between 2 images.
%            If one of the images being compared is regarded as
%            perfect quality, then mssim can be considered as the
%            quality measure of the other image.
%            If img1 = img2, then mssim = 1.
%        (2) ssim_map: the SSIM index map of the test image. The map
%            has a smaller size than the input images. The actual size:
%            size(img1) - size(window) + 1.
%
%Default Usage:
%   Given 2 test images img1 and img2, whose dynamic range is 0-255
%
%   [mssim ssim_map] = ssim_index(img1, img2);
%
%Advanced Usage:
%   User defined parameters. For example
%
%   K = [0.05 0.05];
%   window = ones(8);
%   L = 100;
%   [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
%
%See the results:
%
%   mssim                        %Gives the mssim value
%   imshow(max(0, ssim_map).^4)  %Shows the SSIM index map
%
%========================================================================


if (nargin < 2 || nargin > 5)
    ssim_index = -Inf;
    ssim_map = -Inf;
    return;
end

if (size(img1) ~= size(img2))
    ssim_index = -Inf;
    ssim_map = -Inf;
    return;
end

[M N] = size(img1);

if (nargin == 2)
    if ((M < 11) || (N < 11))
        ssim_index = -Inf;
        ssim_map = -Inf;
        return
    end
    window = fspecial('gaussian', 11, 1.5);	%
    K(1) = 0.01;								      % default settings
    K(2) = 0.03;								      %
    L = 255;                                  %
end

if (nargin == 3)
    if ((M < 11) || (N < 11))
        ssim_index = -Inf;
        ssim_map = -Inf;
        return
    end
    window = fspecial('gaussian', 11, 1.5);
    L = 255;
    if (length(K) == 2)
        if (K(1) < 0 || K(2) < 0)
            ssim_index = -Inf;
            ssim_map = -Inf;
            return;
        end
    else
        ssim_index = -Inf;
        ssim_map = -Inf;
        return;
    end
end

if (nargin == 4)
    [H W] = size(window);
    if ((H*W) < 4 || (H > M) || (W > N))
        ssim_index = -Inf;
        ssim_map = -Inf;
        return
    end
    L = 255;
    if (length(K) == 2)
        if (K(1) < 0 || K(2) < 0)
            ssim_index = -Inf;
            ssim_map = -Inf;
            return;
        end
    else
        ssim_index = -Inf;
        ssim_map = -Inf;
        return;
    end
end

if (nargin == 5)
    [H W] = size(window);
    if ((H*W) < 4 || (H > M) || (W > N))
        ssim_index = -Inf;
        ssim_map = -Inf;
        return
    end
    if (length(K) == 2)
        if (K(1) < 0 || K(2) < 0)
            ssim_index = -Inf;
            ssim_map = -Inf;
            return;
        end
    else
        ssim_index = -Inf;
        ssim_map = -Inf;
        return;
    end
end

C1 = (K(1)*L)^2;
C2 = (K(2)*L)^2;
window = window/sum(sum(window));
img1 = double(img1);
img2 = double(img2);

mu1   = filter2(window, img1, 'valid');
mu2   = filter2(window, img2, 'valid');
mu1_sq = mu1.*mu1;
mu2_sq = mu2.*mu2;
mu1_mu2 = mu1.*mu2;
sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;

if (C1 > 0 & C2 > 0)
    ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2));
else
    numerator1 = 2*mu1_mu2 + C1;
    numerator2 = 2*sigma12 + C2;
    denominator1 = mu1_sq + mu2_sq + C1;
    denominator2 = sigma1_sq + sigma2_sq + C2;
    ssim_map = ones(size(mu1));
    index = (denominator1.*denominator2 > 0);
    ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
    index = (denominator1 ~= 0) & (denominator2 == 0);
    ssim_map(index) = numerator1(index)./denominator1(index);
end

mssim = mean2(ssim_map);

end