基于SRCNN深度神经网络的超分辨率重建算法的matlab仿真，matlab2021a运行测试。

[{"title":"（ 32 个子文件 6.71MB ） 基于SRCNN深度神经网络的超分辨率重建算法的matlab仿真，matlab2021a运行测试。","children":[{"title":"Set5","children":[{"title":"bird_GT.bmp <span style='color:#111;'> 243.05KB </span>","children":null,"spread":false},{"title":"woman_GT.bmp <span style='color:#111;'> 229.83KB </span>","children":null,"spread":false},{"title":"head_GT.bmp <span style='color:#111;'> 229.74KB </span>","children":null,"spread":false},{"title":"butterfly_GT.bmp <span style='color:#111;'> 192.12KB </span>","children":null,"spread":false},{"title":"baby_GT.bmp <span style='color:#111;'> 768.05KB </span>","children":null,"spread":false}],"spread":true},{"title":"Set14","children":[{"title":"man.bmp <span style='color:#111;'> 768.05KB </span>","children":null,"spread":false},{"title":"barbara.bmp <span style='color:#111;'> 1.19MB </span>","children":null,"spread":false},{"title":"coastguard.bmp <span style='color:#111;'> 297.05KB </span>","children":null,"spread":false},{"title":"face.bmp <span style='color:#111;'> 223.23KB </span>","children":null,"spread":false},{"title":"flowers.bmp <span style='color:#111;'> 530.33KB </span>","children":null,"spread":false},{"title":"bridge.bmp <span style='color:#111;'> 257.05KB </span>","children":null,"spread":false},{"title":"ppt3.bmp <span style='color:#111;'> 1017.37KB </span>","children":null,"spread":false},{"title":"monarch.bmp <span style='color:#111;'> 1.13MB </span>","children":null,"spread":false},{"title":"pepper.bmp <span style='color:#111;'> 768.05KB </span>","children":null,"spread":false},{"title":"comic.bmp <span style='color:#111;'> 265.16KB </span>","children":null,"spread":false},{"title":"baboon.bmp <span style='color:#111;'> 703.18KB </span>","children":null,"spread":false},{"title":"zebra.bmp <span style='color:#111;'> 672.08KB </span>","children":null,"spread":false},{"title":"foreman.bmp <span style='color:#111;'> 297.05KB </span>","children":null,"spread":false},{"title":"lenna.bmp <span style='color:#111;'> 768.05KB </span>","children":null,"spread":false}],"spread":false},{"title":"SRCNN.m <span style='color:#111;'> 1.47KB </span>","children":null,"spread":false},{"title":"model","children":[{"title":"9-1-5(ImageNet)","children":[{"title":"x3.mat <span style='color:#111;'> 37.45KB </span>","children":null,"spread":false}],"spread":true},{"title":"9-1-5(91 images)","children":[{"title":"x2.mat <span style='color:#111;'> 37.72KB </span>","children":null,"spread":false},{"title":"x4.mat <span style='color:#111;'> 37.59KB </span>","children":null,"spread":false},{"title":"x3.mat <span style='color:#111;'> 37.58KB </span>","children":null,"spread":false}],"spread":true},{"title":"9-5-5(ImageNet)","children":[{"title":"x2.mat <span style='color:#111;'> 262.63KB </span>","children":null,"spread":false},{"title":"x4.mat <span style='color:#111;'> 262.70KB </span>","children":null,"spread":false},{"title":"x3.mat <span style='color:#111;'> 261.32KB </span>","children":null,"spread":false}],"spread":true},{"title":"9-3-5(ImageNet)","children":[{"title":"x3.mat <span style='color:#111;'> 112.39KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"compute_psnr.m <span style='color:#111;'> 314B </span>","children":null,"spread":false},{"title":"shave.m <span style='color:#111;'> 110B </span>","children":null,"spread":false},{"title":"demo_SR.m <span style='color:#111;'> 1.55KB </span>","children":null,"spread":false},{"title":"modcrop.m <span style='color:#111;'> 279B </span>","children":null,"spread":false}],"spread":true}]