Awesome
Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
WIP: fast evaluation with custom ops
An older version of the NLRN code can be found here.
Usage
Denoising
Preparing BSD500 for training
mkdir -p data/bsd500
wget -O data/bsd500/BSR_bsds500.tgz http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz
`cd data/bsd500 && tar -xvf BSR_bsds500.tgz`
mkdir -p data/bsd500/flist1
find data/bsd500/BSR/BSDS500/data/images/train/*.jpg data/bsd500/BSR/BSDS500/data/images/test/*.jpg > data/bsd500/flist1/train.flist
find data/bsd500/BSR/BSDS500/data/images/val/*.jpg > data/bsd500/flist1/eval.flist
Preparing Set12 and BSD68 for evaluation
git clone https://github.com/cszn/DnCNN.git data/denoise
find data/denoise/testsets/Set12/*.png > data/set12.flist
find data/denoise/testsets/BSD68/*.png > data/bsd68.flist
Training on flist1 (train and test) of BSD500
python trainer.py --dataset denoise --train-flist data/bsd500/flist1/train.flist --eval-flist data/bsd500/flist1/eval.flist --model nlrn --job-dir debug
# or incremental trainer by number of recurrent states
python incremental_trainer.py --dataset denoise --train-flist data/bsd500/flist1/train.flist --eval-flist data/bsd500/flist1/eval.flist --model nlrn --job-dir debug
Pre-trained models
12 recurrent states/with correlation propagation: sigma 15, sigma 25, sigma 50.
15 recurrent states/without correlation propagation: sigma 15, sigma 25, sigma 50.
Prediction on Set12 and BSD68
python -m datasets.denoise --noise-sigma SIGMA --model-dir MODEL_DIR --input-dir data/denoise/testsets/Set12 --output-dir ./output/Set12
python -m datasets.denoise --noise-sigma SIGMA --model-dir MODEL_DIR --input-dir data/denoise/testsets/BSD68 --output-dir ./output/BSD68
MODEL_DIR
is the directory of tf.saved_model
and located in export/Servo/
of job_dir
.
Super-resolution
Preparing Set5 Set14 BSD100 Urban100 for evaluation
wget -O data/SR_testing_datasets.zip http://vllab.ucmerced.edu/wlai24/LapSRN/results/SR_testing_datasets.zip
`cd data/ && unzip SR_testing_datasets.zip`
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}
}