https://github.com/Ding-Liu/NLRN

Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
https://github.com/Ding-Liu/NLRN

image-denoising image-super-resolution

Last synced: about 1 month ago
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Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)

• Host: GitHub
• URL: https://github.com/Ding-Liu/NLRN
• Owner: Ding-Liu
• License: mit
• Fork: true (ychfan/tf_estimator_barebone)
• Created: 2018-10-25T18:32:57.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-04-15T17:14:04.000Z (over 5 years ago)
• Last Synced: 2024-08-02T11:19:20.031Z (4 months ago)
• Topics: image-denoising, image-super-resolution
• Language: Python
• Homepage:
• Size: 43.9 KB
• Stars: 168
• Watchers: 14
• Forks: 39
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-image-denoising-state-of-the-art - [Web - Liu/NLRN) [[PDF]](https://arxiv.org/pdf/1806.02919.pdf) (Denoising Algorithms)

README

        
# Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)

[Paper](http://papers.nips.cc/paper/7439-non-local-recurrent-network-for-image-restoration.pdf) | [Bibtex](#Bibtex)

WIP: fast evaluation with custom ops

An older version of the NLRN code can be found [here](https://github.com/Ding-Liu/NLRN_v0).

## 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](https://drive.google.com/open?id=1cbaLYjPn_H6TRbLPfA6B3j45TmKdUrfa), [sigma 25](https://drive.google.com/open?id=1l_G9wniOKSM4dS8NqGP-SptPVT5PFo6l), [sigma 50](https://drive.google.com/open?id=1UlDZLSb0a-1fpMgi_LRWozpRsOfWIMek).

15 recurrent states/without correlation propagation: [sigma 15](https://github.com/Ding-Liu/NLRN/files/2674584/sigma15.zip), [sigma 25](https://github.com/Ding-Liu/NLRN/files/2674585/sigma25.zip), [sigma 50](https://github.com/Ding-Liu/NLRN/files/2674586/sigma50.zip).

#### 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}

}

```