SR_LapSRN.m

function [img_HR, time] = SR_LapSRN(img_LR, net, scale, gpu)
% -------------------------------------------------------------------------
%   Description:
%       function to apply SR with LapSRN
%
%   Input:
%       - img_LR: low-resolution image
%       - net   : LapSRN model
%       - scale : upsampling scale
%       - gpu   : GPU ID
%
%   Output:
%       - img_HR: high-resolution image
%
%   Citation: 
%       Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
%       Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang
%       IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
%
%   Contact:
%       Wei-Sheng Lai
%       wlai24@ucmerced.edu
%       University of California, Merced
% -------------------------------------------------------------------------

    %% setup
    net.mode = 'test' ;
    output_var = 'level1_output';
    output_index = net.getVarIndex(output_var);
    net.vars(output_index).precious = 1;
    
    % RGB to YUV
    if( size(img_LR, 3) > 1 )
        img_LR = rgb2ycbcr(img_LR);
    end
    
    % extract Y
    y = single(img_LR(:, :, 1));
    
    if( gpu )
        y = gpuArray(y);
    end
    
    % bicubic upsample UV
    img_HR = imresize(img_LR, scale);
    

    % forward
    tic;
    inputs = {'LR', y};
    net.eval(inputs);
    time = toc;
    
    y = gather(net.vars(output_index).value);
        
    % resize if size does not match the output image
    if( size(y, 1) ~= size(img_HR, 1) )
        y = imresize(y, [size(img_HR, 1), size(img_HR, 2)]);
    end
    
    img_HR(:, :, 1) = double(y);
        
    % YUV to RGB
    if( size(img_HR, 3) > 1 )
        img_HR = ycbcr2rgb(img_HR);
    end
        
    
end