【图像重建】基于FSRCNN的图像超分辨重建算法附matlab代码

1 简介

FSＲCNN 模型是汤晓鸥团队设计的一种基于卷积神经网络的单一图像超分辨率重建模型，是对 SＲCNN 模型的改进，SＲCNN 模型首先将一个低分辨率图像通过双三次插值放大到目标大小，再通过 3 层的卷积层做非线性映射，最后重建出高分辨率图像。SＲCNN 的结构包含 3 层的神经网络，如图 1 所示，分别和传统的 SＲ 模型的三个步骤相对应，其中第 1 层卷积对应特征提取和表示，第 2 层卷积对应非线性映射，第 3 层卷积对应最后的重建。

2 部分代码

% =========================================================================% Test code for Fast Super-Resolution Convolutional Neural Networks (FSRCNN)%% Reference%   Chao Dong, Chen Change Loy, Xiaoou Tang. Accelerating the Super-Resolution Convolutional Neural Networks, %   in Proceedings of European Conference on Computer Vision (ECCV), 2016%​% =========================================================================close all;clear all;​%% set parameterstestfolder = 'test\Set5\';up_scale = 3;model = 'model\FSRCNN\x3.mat';​filepaths = dir(fullfile(testfolder,'*.bmp'));psnr_bic = zeros(length(filepaths),1);psnr_fsrcnn = zeros(length(filepaths),1);​for i = 1 : length(filepaths)       %% read ground truth image    [add,imname,type] = fileparts(filepaths(i).name);    im = imread([testfolder imname type]);​    %% work on illuminance only    if size(im,3) > 1        im_ycbcr = rgb2ycbcr(im);        im = im_ycbcr(:, :, 1);    end    im_gnd = modcrop(im, up_scale);    im_gnd = single(im_gnd)/255;    im_l = imresize(im_gnd, 1/up_scale, 'bicubic');​    %% FSRCNN    im_h = FSRCNN(model, im_l, up_scale);​    %% bicubic interpolation    im_b = imresize(im_l, up_scale, 'bicubic');​    %% remove border    if up_scale==3        im_h = shave_x3(uint8(im_h * 255), [up_scale, up_scale]);    else         im_h = shave(uint8(im_h * 255), [up_scale, up_scale]);    end    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(i) = compute_psnr(im_gnd,im_b);    psnr_fsrcnn(i) = compute_psnr(im_gnd,im_h);​    %% save results    imwrite(im_b, [imname '_bic.bmp']);    imwrite(im_h, [imname '_FSRCNN.bmp']);​end​fprintf('Mean PSNR for Bicubic: %f dB\n', mean(psnr_bic));fprintf('Mean PSNR for FSRCNN: %f dB\n', mean(psnr_fsrcnn));​

3 仿真结果

4 参考文献

[1]雷为民, 王玉楠, 李锦环. 基于FSRCNN的图像超分辨率重建算法优化研究[J]. 传感器与微系统, 2020, 39(2):4.

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