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- """
- ## Multi-Stage Progressive Image Restoration
- ## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao
- ## https://arxiv.org/abs/2102.02808
- """
-
- clc;close all;clear all;
-
- % datasets = {'Rain100L'};
- datasets = {'Test100', 'Rain100H', 'Rain100L', 'Test2800', 'Test1200'};
- num_set = length(datasets);
-
- psnr_alldatasets = 0;
- ssim_alldatasets = 0;
-
- tic
- delete(gcp('nocreate'))
- parpool('local',20);
-
- for idx_set = 1:num_set
- file_path = strcat('./results/', datasets{idx_set}, '/');
- gt_path = strcat('./Datasets/test/', datasets{idx_set}, '/target/');
- path_list = [dir(strcat(file_path,'*.jpg')); dir(strcat(file_path,'*.png'))];
- gt_list = [dir(strcat(gt_path,'*.jpg')); dir(strcat(gt_path,'*.png'))];
- img_num = length(path_list);
-
- total_psnr = 0;
- total_ssim = 0;
- if img_num > 0
- parfor j = 1:img_num
- image_name = path_list(j).name;
- gt_name = gt_list(j).name;
- input = imread(strcat(file_path,image_name));
- gt = imread(strcat(gt_path, gt_name));
- ssim_val = compute_ssim(input, gt);
- psnr_val = compute_psnr(input, gt);
- total_ssim = total_ssim + ssim_val;
- total_psnr = total_psnr + psnr_val;
- end
- end
- qm_psnr = total_psnr / img_num;
- qm_ssim = total_ssim / img_num;
-
- fprintf('For %s dataset PSNR: %f SSIM: %f\n', datasets{idx_set}, qm_psnr, qm_ssim);
-
- psnr_alldatasets = psnr_alldatasets + qm_psnr;
- ssim_alldatasets = ssim_alldatasets + qm_ssim;
-
- end
-
- fprintf('For all datasets PSNR: %f SSIM: %f\n', psnr_alldatasets/num_set, ssim_alldatasets/num_set);
-
- delete(gcp('nocreate'))
- toc
-
- function ssim_mean=compute_ssim(img1,img2)
- if size(img1, 3) == 3
- img1 = rgb2ycbcr(img1);
- img1 = img1(:, :, 1);
- end
-
- if size(img2, 3) == 3
- img2 = rgb2ycbcr(img2);
- img2 = img2(:, :, 1);
- end
- ssim_mean = SSIM_index(img1, img2);
- end
-
- function psnr=compute_psnr(img1,img2)
- if size(img1, 3) == 3
- img1 = rgb2ycbcr(img1);
- img1 = img1(:, :, 1);
- end
-
- if size(img2, 3) == 3
- img2 = rgb2ycbcr(img2);
- img2 = img2(:, :, 1);
- end
-
- imdff = double(img1) - double(img2);
- imdff = imdff(:);
- rmse = sqrt(mean(imdff.^2));
- psnr = 20*log10(255/rmse);
-
- 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
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