Awesome
Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
This is the original version of the NLRN code. A newer version can be found here.
Usage
Denoising
Preparing 400 images from BSDS500 for training
Under the root directory of this repository
mkdir -p data
Download the compressed 400 training images in grayscale here. They are converted from the color images in BSDS500. Move the compressed file BSDS500.tar.gz
to ./data
Uncompress them and generate the training file name list:
cd data
tar -zxf BSDS500.tar.gz
cd BSDS500
find train_gray_rgb2gray/*.png test_gray_rgb2gray/*.png > ../train.list
Preparing Set12 and BSD68 for evaluation
These two datasets can be downloaded from here. Move them to ./data
Training on 400 images (train and test) of BSD500
Under the root directory of this repository
bash train.sh
Released models
Prediction on Set12 and BSD68
Unzip the downloaded files and move them under ./checkpoints
bash test.sh
Image Super-Resolution
Released models
The model can be downloaded here.
Preparing Set5 and Set14 for evaluation
These two datasets can be downloaded from here. Move them to ./data
Prediction on Set5 and Set14
Unzip the downloaded files and move them under ./checkpoints
bash test_sr.sh
Dependencies
- Python 2.7
- TensorFlow 1.10
Bibtex
@inproceedings{liu2018non,
title={Non-Local Recurrent Network for Image Restoration},
author={Liu, Ding and Wen, Bihan and Fan, Yuchen and Loy, Chen Change and Huang, Thomas S},
booktitle={Advances in Neural Information Processing Systems},
pages={1680--1689},
year={2018}
}