train_dictionaries_3.zip_train_图像超分辨率_基于字典算法_字典 超分辨率_字典训练

%% Sparse reconstruction approach code: file 1 % EE368 project 2011: Ritesh Kolte and Abhishek Arora % training dictionaries up_factor = 3; addpath('C:\Users\ritesh\Documents\MATLAB\ee 368\project\sparse\SR pairs'); im1 = imread('im1_small.png'); im1 = rgb2ycbcr(im1); im1 = double(im1(:,:,1)); im2 = imread('im2_small.png'); im2 = rgb2ycbcr(im2); im2 = double(im2(:,:,1)); im3 = imread('im3_small.png'); im3 = rgb2ycbcr(im3); im3 = double(im3(:,:,1)); im1_SR = imread('im1_SR_3.png'); im1_SR = rgb2ycbcr(im1_SR); im1_SR = double(im1_SR(6:end-5,6:end-5,1)); im2_SR = imread('im2_SR_3.png'); im2_SR = rgb2ycbcr(im2_SR); im2_SR = double(im2_SR(6:end-5,6:end-5,1)); im3_SR = imread('im3_SR_3.png'); im3_SR = rgb2ycbcr(im3_SR); im3_SR = double(im3_SR(6:end-5,6:end-5,1)); im1_low = imresize(im1,2,'bicubic'); im2_low = imresize(im2,2,'bicubic'); im3_low = imresize(im3,2,'bicubic'); %% image1 dim = size(im1_low); num_training = (length(1:6:dim(1)-5))*(length(1:6:dim(2)-5)); Xh1 = zeros(81,num_training); Yl1 = zeros(36,num_training); num = 0; for m = 1:6:dim(1)-5 for n = 1:6:dim(2)-5 mean = mean2(im1_low(m:m+5,n:n+5)); Yl1(:,num+1) = reshape(im1_low(m:m+5,n:n+5)-mean,[],1); Xh1(:,num+1) = reshape(im1_SR(((m-1)*3/2)+1:((m-1)*3/2)+9,((n-1)*3/2)+1:((n-1)*3/2)+9)-mean,[],1); num = num+1; end end %% image2 dim = size(im2_low); num_training = (length(1:6:dim(1)-5))*(length(1:6:dim(2)-5)); Xh2 = zeros(81,num_training); Yl2 = zeros(36,num_training); num = 0; for m = 1:6:dim(1)-5 for n = 1:6:dim(2)-5 mean = mean2(im2_low(m:m+5,n:n+5)); Yl2(:,num+1) = reshape(im2_low(m:m+5,n:n+5)-mean,[],1); Xh2(:,num+1) = reshape(im2_SR(((m-1)*3/2)+1:((m-1)*3/2)+9,((n-1)*3/2)+1:((n-1)*3/2)+9)-mean,[],1); num = num+1; end end %% image3 dim = size(im3_low); num_training = (length(1:6:dim(1)-5))*(length(1:6:dim(2)-5)); Xh3 = zeros(81,num_training); Yl3 = zeros(36,num_training); num = 0; for m = 1:6:dim(1)-5 for n = 1:6:dim(2)-5 mean = mean2(im3_low(m:m+5,n:n+5)); Yl3(:,num+1) = reshape(im3_low(m:m+5,n:n+5)-mean,[],1); Xh3(:,num+1) = reshape(im3_SR(((m-1)*3/2)+1:((m-1)*3/2)+9,((n-1)*3/2)+1:((n-1)*3/2)+9)-mean,[],1); num = num+1; end end %% form dictionaries Yl = [Yl1 Yl2 Yl3]; Xh = [Xh1 Xh2 Xh3]; clear Yl1 Yl2 Yl3 Xh1 Xh2 Xh3 clear im* dim mean num m n num_training Dl = Yl; Dh = Xh; clear Xh Yl % We tried solving the biconvex optimization problem below, but later % decided not to use it since we didn't have enough training data and using % raw patches as dictionaries was giving good results. % % clear low_grad* % % save('dictionaries') % N = 81; % M = 36; % X_total = [Xh/sqrt(N); Yl/sqrt(M)]; % % beta = 25*(1/sqrt(N) + 1/sqrt(M)); % beta = 4; % num_iters = 2; % num_bases = 128; % Binit = randn(81+36,num_bases); % normalize = sqrt(diag(Binit'*Binit)); % Binit = Binit./(ones(81+36,1)*normalize'); % % [B S stat] = sparse_coding(X_total, num_bases, beta, 'L1', [], num_iters, 1000, 'dictionaries', [], Binit); % % Dh = B(1:81,:)/sqrt(N); % Dl = B(82:end,:)/sqrt(M); % % clear B Binit M N S X_total Xh Yl beta normalize num_iters stat