SRCNN
所属分类：其他
开发工具：matlab
文件大小：7023KB
下载次数：15
上传日期：2017-09-19 19:32:31
上 传 者：
COCO-2017
说明： SRCNN超分辨率重构的matlab应用
(Matlab application of SRCNN super-resolution reconstruction)
文件列表:
SRCNN (0, 2017-09-19)
SRCNN\compute_psnr.m (314, 2014-09-02)
SRCNN\demo_SR.m (1910, 2017-09-18)
SRCNN\MATLAB下神经网络工具箱的开发和应用_刘晔.pdf (274291, 2017-09-19)
SRCNN\modcrop.m (300, 2017-09-18)
SRCNN\model (0, 2017-09-14)
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\picture (0, 2017-09-18)
SRCNN\picture\eye.PNG (13431, 2017-09-18)
SRCNN\picture\people.jpg (26674, 2017-09-18)
SRCNN\Set14 (0, 2017-09-14)
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, 2017-09-14)
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 (1568, 2017-09-18)
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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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