19.02.2020

ImageLobe 5.5

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5.5

Imagelobe 5.5 Weeks

%
% Test code for Super-Resolution Convolutional Neural Networks (SRCNN)
%
% Reference
% Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Learning a Deep Convolutional Network for Image Super-Resolution,
% in Proceedings of European Conference on Computer Vision (ECCV), 2014
%
% Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Image Super-Resolution Using Deep Convolutional Networks,
% arXiv:1501.00092
%
% Chao Dong
% IE Department, The Chinese University of Hong Kong
% For any question, send email to ndc.forward@gmail.com
%
close all;
clear all;
%% read ground truth image
im = imread('Set5/butterfly_GT.bmp');
%im = imread('Set14zebra.bmp');
%% set parameters
up_scale = 4;
model = 'model/9-5-5(ImageNet)/x4.mat';
% up_scale = 3;
% model = 'model9-3-5(ImageNet)x3.mat';
% up_scale = 3;
% model = 'model9-1-5(91 images)x3.mat';
% up_scale = 2;
% model = 'model9-5-5(ImageNet)x2.mat';
% up_scale = 4;
% model = 'model9-5-5(ImageNet)x4.mat';
%% work on illuminance only
if size(im,3)>1
im = rgb2ycbcr(im);
im = im(:, :, 1);
end
im_gnd = modcrop(im, up_scale);
im_gnd = single(im_gnd)/255;
%% bicubic interpolation
im_l = imresize(im_gnd, 1/up_scale, 'bicubic');
im_b = imresize(im_l, up_scale, 'bicubic');
%% SRCNN
im_h = SRCNN(model, im_b);
%% remove border
im_h = shave(uint8(im_h *255), [up_scale, up_scale]);
im_gnd = shave(uint8(im_gnd *255), [up_scale, up_scale]);
im_b = shave(uint8(im_b *255), [up_scale, up_scale]);
%% compute PSNR
psnr_bic = compute_psnr(im_gnd,im_b);
psnr_srcnn = compute_psnr(im_gnd,im_h);
%% show results
fprintf('PSNR for Bicubic Interpolation: %f dBn', psnr_bic);
fprintf('PSNR for SRCNN Reconstruction: %f dBn', psnr_srcnn);
figure, imshow(im_b); title('Bicubic Interpolation');
figure, imshow(im_h); title('SRCNN Reconstruction');
%imwrite(im_b, ['Bicubic Interpolation' '.bmp']);
%imwrite(im_h, ['SRCNN Reconstruction' '.bmp']);

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