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Evaluate_PSNR_SSIM.m
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Evaluate_PSNR_SSIM.m
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function Evaluate_PSNR_SSIM()
clear all; close all; clc
%% set path
degradation = 'BI'; % BI, BD
methods = {'R2R_NCS2_CASA2_SE_TRANSF6_S_X3'};
dataset = {'BSDS100'};
ext = {'*.jpg', '*.png', '*.bmp'};
num_method = length(methods);%num_method=1
num_set = length(dataset);
record_results_txt = ['PSNR_SSIM_Results_', degradation,'_model.txt'];%输出txt文件PSNR_SSIM_Results_degradation(BI)_model.txt
results = fopen(fullfile(record_results_txt), 'wt');%测试结果写入txt文件
if strcmp(degradation, 'BI')
scale_all = 3;
else
scale_all = 2;
end
for idx_method = 1:num_method
%idx_method=1,2
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});%输出 Method_1: MSRN; Set: Set5
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})));%输出类似这种:./HR/Set5/x2/的图片文件
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;
if strcmp(dataset{idx_set},'urban100') == 1
name_SR_1 = strrep(name_HR, '_HR_x3','');
name_SR = erase(name_SR_1,'_');
else
name_SR = strrep(name_HR, '_HR_x3','');
end
% name_SR = strrep(name_HR, '_HR_x2','x2');
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));%输出类似这种:./SR/BI/methods(文件名为方法名)/Set5/x2/
% 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