https://github.com/monyone/sisr

Single Image Super Resolution (SISR) implementation by PyTorch
https://github.com/monyone/sisr

Last synced: 29 days ago
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Single Image Super Resolution (SISR) implementation by PyTorch

• Host: GitHub
• URL: https://github.com/monyone/sisr
• Owner: monyone
• License: mit
• Created: 2023-06-10T06:01:51.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-04T21:54:58.000Z (about 1 year ago)
• Last Synced: 2024-10-16T10:45:02.297Z (3 months ago)
• Language: Python
• Size: 131 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Single Image Super Resolution (SISR)

## Requirement

* python3

* pytorch

* torchvision

## Models

* [SRCNN (ECCV 2014)](./models/SRCNN/) [[Paper]](https://arxiv.org/abs/1501.00092)

* [FSRCNN (ECCV 2016)](./models/FSRCNN/) [[Paper]](https://arxiv.org/abs/1608.00367)

* [ESPCN (CVPR 2016)](./models/ESPCN/) [[Paper]](https://arxiv.org/abs/1609.05158)

* [VDSR (CVPR 2016)](./models/VDSR/) [[Paper]](https://arxiv.org/abs/1511.04587)

* [DRCN (CVPR Oral 2016)](./models/DRCN/) [[Paper]](https://arxiv.org/abs/1511.04491)

* [RED-Net (CVPR 2016)](./models/REDNet/) [[Paper]](https://arxiv.org/abs/1603.09056)

* [DRRN (CVPR 2017)](./models/DRRN/) [[Paper]](https://ieeexplore.ieee.org/document/8099781)

* [LapSRN (CVPR 2017)](./models/LapSRN/) [[Paper]](https://arxiv.org/abs/1704.03915)

* [SRResNet, SRGAN (CVPR Oral 2017)](./models/SRGAN/) [[Paper]](https://arxiv.org/abs/1609.04802)

* [EDSR (CVPR Workshop 2017)](./models/EDSR/) [[paper]](https://arxiv.org/abs/1707.02921)

* [EnhanceNet (ICCV 2017)](./models/EnhanceNet/) [[Paper]](https://arxiv.org/abs/1612.07919)

* [SRDenseNet (ICCV 2017)](./models/SRDenseNet/) [[Paper]](https://ieeexplore.ieee.org/document/8237776)

* [MSLapSRN (TPAMI 2018)](./models/MSLapSRN/) [[Paper]](https://arxiv.org/abs/1710.01992)

* [TSRN (ECCV 2018)](./models/TSRN/) [[Paper]](https://arxiv.org/abs/1808.00043)

* [RDN (CVPR 2018)](./models/RDN/) [[Paper]](https://arxiv.org/abs/1802.08797)

* [RRDBNet, ESRGAN (ECCV 2018)](./models/ESRGAN/) [[Paper]](https://arxiv.org/abs/1809.00219)

* [RCAN (ECCV 2018)](./models/RCAN/) [[Paper]](https://arxiv.org/abs/1807.02758)

* [SAN (CVPR 2019)](./models/SAN/) [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/html/Dai_Second-Order_Attention_Network_for_Single_Image_Super-Resolution_CVPR_2019_paper.html)

* [Swift-SRGAN (2021)](./models/SwiftSRGAN/) [[Paper]](https://arxiv.org/abs/2111.14320)

* [Real-ESRNet, Real-ESRGAN (ICCV Workshop 2021)](./models/RealESRGAN/) [[Paper]](https://arxiv.org/abs/2107.10833)

* [HPUN (AAAI 2023)](./models/HPUN/) [[Paper]](https://arxiv.org/abs/2203.08921)

## Usage

### Train

#### Geneator

```bash

./train.py --generator [ONE_OF_ABOVE_MODELS] --epoch ${epochs} --scale ${upscale_factor}

```

#### GAN

```bash

./train.py --generator [ONE_OF_ABOVE_MODELS] --discriminator [ONE_OF_ABOVE_DISCRIMINATIOR] --epoch ${epochs} --scale ${upscale_factor} --distort [Real-ESRGAN|BSRGAN|JPEG]

```

### Testing

```bash

./main.py --model [ONE_OF_ABOVE_MODELS] --image ${image_path} --state ${state_path} --scale ${upscale_factor} --test --distort [Real-ESRGAN|BSRGAN|JPEG]

```

### Super Resolution

```bash

./main.py --model [ONE_OF_ABOVE_MODELS] --image ${image_path} --state ${state_path} --scale ${upscale_factor}

```