SRCNN
所属分类：人工智能/神经网络/深度学习
开发工具：matlab
文件大小：6721KB
下载次数：40
上传日期：2016-06-11 15:55:57
上 传 者：
3999147
说明： 深度卷积神经网络超分辨，来自于ECCV2014一篇文章的代码，代码好用
(Depth of the convolutional neural network super resolution, the ECCV2014 an article of the code, code easy to use)
文件列表:
SRCNN (0, 2016-03-30)
SRCNN\compute_psnr.m (314, 2014-09-02)
SRCNN\demo_SR.m (1827, 2014-09-03)
SRCNN\modcrop.m (279, 2014-03-27)
SRCNN\model (0, 2016-03-30)
SRCNN\model\x2.mat (38625, 2014-09-02)
SRCNN\model\x3.mat (38483, 2014-09-01)
SRCNN\model\x4.mat (38489, 2014-09-01)
SRCNN\Set14 (0, 2016-03-30)
SRCNN\Set14\baboon.bmp (720054, 2013-10-06)
SRCNN\Set14\barbara.bmp (1244214, 2013-10-06)
SRCNN\Set14\bridge.bmp (263222, 2013-10-06)
SRCNN\Set14\coastguard.bmp (304182, 2013-10-06)
SRCNN\Set14\comic.bmp (271526, 2013-10-06)
SRCNN\Set14\face.bmp (228584, 2013-10-06)
SRCNN\Set14\flowers.bmp (543054, 2013-10-06)
SRCNN\Set14\foreman.bmp (304182, 2013-10-06)
SRCNN\Set14\lenna.bmp (786486, 2013-10-06)
SRCNN\Set14\man.bmp (786486, 2013-10-06)
SRCNN\Set14\monarch.bmp (1179702, 2013-10-06)
SRCNN\Set14\pepper.bmp (786486, 2013-10-06)
SRCNN\Set14\ppt3.bmp (1041782, 2013-10-06)
SRCNN\Set14\zebra.bmp (688214, 2013-10-06)
SRCNN\Set5 (0, 2016-03-30)
SRCNN\Set5\baby_GT.bmp (786486, 2013-10-06)
SRCNN\Set5\bird_GT.bmp (248886, 2013-10-06)
SRCNN\Set5\butterfly_GT.bmp (196730, 2013-10-06)
SRCNN\Set5\head_GT.bmp (235254, 2013-10-06)
SRCNN\Set5\woman_GT.bmp (235350, 2013-10-06)
SRCNN\shave.m (110, 2013-10-06)
SRCNN\SRCNN.m (1267, 2014-09-02)
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Matlab demo code for "Learning a Deep Convolutional Network for Image Super-Resolution" (ECCV 2014)
by Chao Dong (ndc.forward@gmail.com)
If you use/adapt our code in your work (either as a stand-alone tool or as a component of any algorithm),
you need to appropriately cite our ECCV 2014 paper.
This code is for academic purpose only. Not for commercial/industrial activities.
NOTE:
The running time reported in the paper is from C++ implementation. This Matlab version is a re-
implementation, and is for the ease of understanding the algorithm. This code is not optimized, and the
speed is not representative. The result can be slightly different from the paper due to transferring
across platforms.
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Usage:
demo_SR.m - demonstrate super-resolution using SRCNN.m
function:
SRCNN.m - realize super resolution given the model parameters
Folders:
Set5 and Set14 - test images (Sec.4 in the paper)
Model - "x2.mat" "x3.mat" and "x4.mat" are model parameters used for upscaling factors 2,3 and 4 seperately
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