Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
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This is the testing code for the papers:
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections, arXiv, 2016
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For any question, please send email to [email protected] or, [email protected]
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If you use this code in your research, please cite our papers:
@InProceedings{NIPS2016Mao,
author = "Xiao{-}Jiao Mao and Chunhua Shen and Yu{-}Bin Yang",
title = "Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections",
booktitle = "Proc. Advances in Neural Inf. Process. Syst.",
year = "2016",
}
@Article{MaoSY16a,
author = "Xiao{-}Jiao Mao and Chunhua Shen and Yu{-}Bin Yang",
title = "Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections",
journal = "arXiv preprint",
volume = "abs/1606.08921",
year = "2016",
url = "http://arxiv.org/abs/1606.08921",
timestamp = "Fri, 01 Jul 2016 17:39:49 +0200",
biburl = "http://dblp.uni-trier.de/rec/bib/journals/corr/MaoSY16a",
}
Caffe is required for running this code. For convenience, it is included in the folder Caffe and pre-compiled in Ubuntu 14.04.
The folder model contains the network definition in .prototxt and the trained weights in .caffemodel for different tasks.
The folder utils contains the functions used for image restoration.
The file demo_denoising.m shows that how to use the code for image denoising.
The file demo_super_resolution.m shows that how to use the code for image super-resolution.
The file demo_jpeg_deblocking.m shows that how to use the code for JPEG-deblocking.
The file demo_debluring.m shows that how to use the code for non-blind image debluring.
The file demo_inpainting.m shows an example for scratch removal.
Kindly note that the ``input_dim" of the network should be adapted to datasets.
Copyright (c) Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang. 2016.
** This code is for non-commercial purposes only. For commerical purposes, please contact Xiao-Jiao Mao [email protected] and Chunhua Shen [email protected] **
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.