https://github.com/kozistr/natsr-pytorch
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019) in pytorch
https://github.com/kozistr/natsr-pytorch
cvpr2019 generative-adversarial-network pytorch sisr super-resolution
Last synced: 3 months ago
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Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019) in pytorch
- Host: GitHub
- URL: https://github.com/kozistr/natsr-pytorch
- Owner: kozistr
- License: mit
- Created: 2020-03-31T05:02:47.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T09:54:49.000Z (almost 3 years ago)
- Last Synced: 2023-03-01T07:22:39.428Z (over 2 years ago)
- Topics: cvpr2019, generative-adversarial-network, pytorch, sisr, super-resolution
- Language: Python
- Size: 1020 KB
- Stars: 7
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NatSR-pytorch
Unofficial implementation of natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019) in pytorch (w/ audit-friendly code)
* official **tensorflow** implementation : [https://github.com/JWSoh/NatSR](https://github.com/JWSoh/NatSR)
* paper : [CVPR2019](http://openaccess.thecvf.com/content_CVPR_2019/papers/Soh_Natural_and_Realistic_Single_Image_Super-Resolution_With_Explicit_Natural_Manifold_CVPR_2019_paper.pdf)
**Work In Progress (WIP)**
## Environments
* Python 3.x (recommended 3.7)
* Pytorch 1.x
## Abstract
Recently, many convolutional neural networks for single image super-resolution (SISR) have been proposed, which focus on reconstructing the high-resolution images in terms of objective distortion measures.
**However**, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality.
Recovering the realistic details remains a challenging problem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures.
**However**, the generated fake details often make undesirable artifacts and the overall image looks somewhat unnatural.
**Therefore**, in this paper, we present a new approach to reconstructing realistic super-resolved images with high perceptual quality, while maintaining the naturalness of the result.
*In particular*, we focus on the domain prior properties of SISR problem.
Specifically, we define the naturalness prior in the low-level domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images.
Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones.
## DataSet
* DIV2K : [download](https://data.vision.ee.ethz.ch/cvl/DIV2K/)
## Usage
0. **Clone** the repository
```shell script
$ git clone https://github.com/kozistr/NatSR-pytorch
$ cd ./NatSR-pytorch
```
1. **Configure** your own environment.
1.1. Using `pipenv` by given *Pipfile*
```shell script
$ pip3 install -U pipenv
$ pipenv install --dev
```
1.2. Using `requirements.txt`
```shell script
$ pip3 install -r requirements.txt
```
2. **Change** the parameter what you want [`config.yaml`](./config.yaml)
2.1. Mode
* At train : `mode: train`
* At test : `mode: test`
* At inference : `mode: inference`
2.2. Model Type
* training *NMD* : `model_type: 'nmd'`
* training *FRSR* : `model_type: 'frsr'`
3. Run!
```shell script
$ python3 -m natsr
```
## Result
## Citation
```
@InProceedings{Soh_2019_CVPR,
author = {Soh, Jae Woong and Park, Gu Yong and Jo, Junho and Cho, Nam Ik},
title = {Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
```
## Author
Hyeongchan Kim / [@kozistr](http://kozistr.tech)