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目录 1背景 2.BI 3.BD 4.DN 5总结 1背景超分辨率重建中经典的生成降质图像通常使用MATLAB实现的，通常有四种方法： 类型说明BIbicubic-downBDblur-downBNbicubic-down+noiseDNblur-down+noise其中最常用的是BI，其次是BD，最后是DN。那我将其公认实现和出处总结起来，供大家参考。 注：在一些数据集中由于尺寸问题不能够被下采样的倍率所整除，导致生成数据用于训练测试时会出现很多麻烦，因此注意裁剪其大小。详细见下面代码。 注：关于其Python实现，可同时参考链接 图像/视频超分之降质过程和我的总结图像超分辨率数据集这一篇足够了，你需要注意什么？_Alocus_的博客-CSDN博客_图像超分辨率数据集 2.BI下面是通过MATLAB公认利用bicubic生成低分辨率（BI）方法，摘自xinntao大大 https://github.com/XPixelGroup/BasicSR/blob/479ec97e8a23e49cc559c081f8ff80eac1bc5989/scripts/matlab_scripts/generate_bicubic_img.mhttps://github.com/XPixelGroup/BasicSR/blob/479ec97e8a23e49cc559c081f8ff80eac1bc5989/scripts/matlab_scripts/generate_bicubic_img.m function generate_bicubic_img() %% matlab code to genetate mod images, bicubic-downsampled images and %% bicubic_upsampled images %% set configurations % comment the unnecessary lines input_folder = '../../datasets/Set5/original'; save_mod_folder = '../../datasets/Set5/GTmod12'; save_lr_folder = '../../datasets/Set5/LRbicx2'; % save_bic_folder = ''; mod_scale = 12; up_scale = 2; if exist('save_mod_folder', 'var') if exist(save_mod_folder, 'dir') disp(['It will cover ', save_mod_folder]); else mkdir(save_mod_folder); end end if exist('save_lr_folder', 'var') if exist(save_lr_folder, 'dir') disp(['It will cover ', save_lr_folder]); else mkdir(save_lr_folder); end end if exist('save_bic_folder', 'var') if exist(save_bic_folder, 'dir') disp(['It will cover ', save_bic_folder]); else mkdir(save_bic_folder); end end idx = 0; filepaths = dir(fullfile(input_folder,'*.*')); for i = 1 : length(filepaths) [paths, img_name, ext] = fileparts(filepaths(i).name); if isempty(img_name) disp('Ignore . folder.'); elseif strcmp(img_name, '.') disp('Ignore .. folder.'); else idx = idx + 1; str_result = sprintf('%d\t%s.\n', idx, img_name); fprintf(str_result); % read image img = imread(fullfile(input_folder, [img_name, ext])); img = im2double(img); % modcrop img = modcrop(img, mod_scale); if exist('save_mod_folder', 'var') imwrite(img, fullfile(save_mod_folder, [img_name, '.png'])); end % LR im_lr = imresize(img, 1/up_scale, 'bicubic'); if exist('save_lr_folder', 'var') imwrite(im_lr, fullfile(save_lr_folder, [img_name, '.png'])); end % Bicubic if exist('save_bic_folder', 'var') im_bicubic = imresize(im_lr, up_scale, 'bicubic'); imwrite(im_bicubic, fullfile(save_bic_folder, [img_name, '.png'])); end end end end %% modcrop function img = modcrop(img, modulo) if size(img,3) == 1 sz = size(img); sz = sz - mod(sz, modulo); img = img(1:sz(1), 1:sz(2)); else tmpsz = size(img); sz = tmpsz(1:2); sz = sz - mod(sz, modulo); img = img(1:sz(1), 1:sz(2),:); end end 3.BD下面是通过MATLAB使用公认的模糊（BD）方法生成低分辨率代码。摘自RDN https://github.com/yulunzhang/RDN/blob/master/RDN_TrainCode/Prepare_TrainData/Prepare_TrainData_HR_LR_BD.mhttps://github.com/yulunzhang/RDN/blob/master/RDN_TrainCode/Prepare_TrainData/Prepare_TrainData_HR_LR_BD.m function Prepare_TrainData_HR_LR_BD() %% settings path_save = './DIV2K'; path_src = './DIV2K/DIV2K_HR'; ext = {'*.jpg','*.png','*.bmp'}; filepaths = []; for i = 1 : length(ext) filepaths = cat(1,filepaths, dir(fullfile(path_src, ext{i}))); end nb_im = length(filepaths); DIV2K_HR = []; for idx_im = 1:nb_im fprintf('Read HR :%d\n', idx_im); ImHR = imread(fullfile(path_src, filepaths(idx_im).name)); DIV2K_HR{idx_im} = ImHR; end %% generate and save LR via imresize() with Bicubic kernelsize = 7; sigma = 1.6; for IdxIm = 1:nb_im fprintf('IdxIm=%d\n', IdxIm); ImHR = DIV2K_HR{IdxIm}; ImLRx3 = imresize_BD(ImHR, 3, kernelsize, sigma); % name image digit = IdxIm; fileName = num2str(IdxIm); while digit < 1000 fileName = ['0', fileName]; digit = digit*10; end FolderLRx3 = fullfile(path_save, 'DIV2K_LR_bicubic', 'X3'); if ~exist(FolderLRx3) mkdir(FolderLRx3) end NameLRx3 = fullfile(FolderLRx3, [fileName, 'x3BD.png']); % save image imwrite(ImLRx3, NameLRx3, 'png'); end end function ImLR = imresize_BD(ImHR, scale, kernelsize, sigma) % ImLR and ImHR are uint8 data % downsample by Bicubic kernel = fspecial('gaussian',kernelsize,sigma); blur_HR = imfilter(ImHR,kernel,'replicate'); ImLR = imresize(blur_HR, 1/scale, 'nearest'); end 4.DN下面是DN的 MATLAB实现 function Prepare_TrainData_HR_LR_DN() %% settings path_save = './DIV2K'; path_src = './DIV2K/DIV2K_HR'; ext = {'*.jpg','*.png','*.bmp'}; filepaths = []; for i = 1 : length(ext) filepaths = cat(1,filepaths, dir(fullfile(path_src, ext{i}))); end nb_im = length(filepaths); DIV2K_HR = []; for idx_im = 1:nb_im fprintf('Read HR :%d\n', idx_im); ImHR = imread(fullfile(path_src, filepaths(idx_im).name)); DIV2K_HR{idx_im} = ImHR; end %% generate and save LR via imresize() with Bicubic sigma = 30; % noise level for IdxIm = 1:nb_im fprintf('IdxIm=%d\n', IdxIm); ImHR = DIV2K_HR{IdxIm}; ImLRx3 = imresize_DN(ImHR, 3, sigma); % name image digit = IdxIm; fileName = num2str(IdxIm); while digit < 1000 fileName = ['0', fileName]; digit = digit*10; end FolderLRx3 = fullfile(path_save, 'DIV2K_LR_bicubic', 'X3'); if ~exist(FolderLRx3) mkdir(FolderLRx3) end NameLRx3 = fullfile(FolderLRx3, [fileName, 'x3DN.png']); % save image imwrite(ImLRx3, NameLRx3, 'png'); end end function ImLR = imresize_DN(ImHR, scale, sigma) % ImLR and ImHR are uint8 data % downsample by Bicubic ImDown = imresize(ImHR, 1/scale, 'bicubic'); % 0-255 ImDown = single(ImDown); % 0-255 ImDownNoise = ImDown + single(sigma*randn(size(ImDown))); % 0-255 ImLR = uint8(ImDownNoise); % 0-255 end 5总结关于Python实现，以及详细信息，我还是想建议大家参考这边文章，总结的很不错，感谢稳扎那股作者以及参考文献中的作者们的贡献。！ 图像/视频超分之降质过程 |
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