caomw/2016_super_resolution

super resolution 2015ICCV Image Super-Resolution Using Deep Convolutional Networks

2016_super_resolution

ICCV2015 Image Super-Resolution Using Deep Convolutional Networks I include train and test code in master branch.

Training data

I random selected about 60,000 pic from 2014 ILSVR2014_train (only academic) You can download from https://pan.baidu.com/s/1c0TvFyw

Result

This code get the better performance than 'bicubic' for enlarging a 2x pic. It can be trained and tested now.

original pic -> super resolution pic (trained by matconvnet)

How to train & test

1.You may compile matconvnet first by running gpu_compile.m (you need to change some setting in it)

For more compile information, you can learn it from www.vlfeat.org/matconvnet/install/#compiling

2.run testSRnet_result.m for test result.

3.If you want to train it by yourself, you may download my data and use prepare_ur_data.m to produce imdb.mat which include every picture path.

4.Use train_SRnet.m to have fun~

Improvement

1.I add rmsprop to matconvnet(You can learn more from /matlab/cnn_daga.m)

2.I fix the scale factor 2(than 2+2*rand). It seems to be easy for net to learn more information.

3.How to initial net? (You can learn more from /matlab/+dagnn/@DagNN/initParam.m) In this work, the initial weight is important