BSRGANx2
BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .
Model source
- Kind
- Other
- File size
- 63.8 MB
- Version
- vVersion 1
- Updated
- Oct 18, 2026
- Source
- Hugging Face
Source description
Model Description
BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .
BSRGAN-Pip: Packaged version of the BSRGAN repository
Paper Repo: Implementation of paper - BSRGAN
Installation
pip install bsrgan
BSRGAN Usage
from bsrgan import BSRGAN
model = BSRGAN(weights='kadirnar/BSRGANx2', device='cuda:0', hf_model=True)
model.save = True
pred = model.predict(img_path='data/image/test.png')
BibTeX Entry and Citation Info
@inproceedings{zhang2021designing,
title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution},
author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu},
booktitle={IEEE International Conference on Computer Vision},
pages={4791--4800},
year={2021}
}
Sources
1 source
Sources
1 sourceVerified Oct 18
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 tags: - Super-Resolution - computer-vision - bsrgan - gan --- ### Model Description [BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution .](https://arxiv.org/abs/2103.14006) [BSRGAN-Pip: Packaged version of the BSRGAN repository](https://github.com/kadirnar/bsrgan-pip/) [Paper Repo: Implementation of paper - BSRGAN](https://github.com/cszn/BSRGAN) ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/BSRGANx2', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ```
Source context: 0 downloads · 2 likes · Repo kadirnar/BSRGANx2
