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https://github.com/isaaccorley/pytorch-enhance

Open-source Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use
https://github.com/isaaccorley/pytorch-enhance

artificial-intelligence computer-vision deep-learning pytorch super-resolution

Last synced: 24 days ago
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Open-source Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use

Host: GitHub
URL: https://github.com/isaaccorley/pytorch-enhance
Owner: isaaccorley
License: apache-2.0
Created: 2019-12-28T22:23:10.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2021-09-03T11:55:19.000Z (about 3 years ago)
Last Synced: 2024-10-14T10:15:26.138Z (about 1 month ago)
Topics: artificial-intelligence, computer-vision, deep-learning, pytorch, super-resolution
Language: Python
Homepage:
Size: 29.1 MB
Stars: 75
Watchers: 4
Forks: 11
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        ![](assets/pytorch-enhance-logo-cropped.png)

# pytorch-enhance: Image Super-Resolution in PyTorch

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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3739368.svg)](https://doi.org/10.5281/zenodo.3739368)

Library for Minimal Modern Image Super-Resolution in PyTorch

--------------------------------------------------------------------------------

PyTorch Enhance provides a consolidated package of popular Image Super-Resolution models, datasets, and metrics to allow for quick and painless benchmarking or for quickly adding pretrained models to your application.

## Documentation

[https://pytorch-enhance.readthedocs.io](https://pytorch-enhance.readthedocs.io)

## Installation

### pip

```

pip install torch-enhance

```

### latest

```

git clone https://github.com/IsaacCorley/pytorch-enhance.git

cd pytorch-enhance

python setup.py install

```

## Models

The following models are currently implemented:

* **SRCNN** from Dong et. al [Image Super-Resolution Using Deep Convolutional Networks](https://arxiv.org/pdf/1501.00092v3.pdf)

* **VDSR** from Lee et al. [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://arxiv.org/pdf/1511.04587.pdf)

* **ESPCN** from Shi et. al [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/pdf/1609.05158v2.pdf)

* **SRResNet** from Ledig et. al [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://arxiv.org/pdf/1609.04802v5.pdf)

* **EDSR** from Lim et. al [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/pdf/1707.02921v1.pdf)

```python

import torch

import torch_enhance

# increase resolution by factor of 2 (e.g. 128x128 -> 256x256)

model = torch_enhance.models.SRResNet(scale_factor=2, channels=3)

lr = torch.randn(1, 3, 128, 128)

sr = model(x) # [1, 3, 256, 256]

```

## State-of-the-Art

Not sure which models are currently the best? Check out the [PapersWithCode Image Super-Resolution Leaderboards](https://paperswithcode.com/task/image-super-resolution)

## Datasets

The following benchmark datasets are available for usage:

* **[BSDS100](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[BSDS200](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[BSDS300](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)**

* **[BSDS500](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html)**

* **[Set5](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[Set14](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[T91](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[Historical](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[Urban100](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[Manga109](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[General100](https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU)**

* **[DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/)**

## Dataset Samples

**BSDS300**                 |  **BSDS500**              |   **T91**

:-------------------------:|:-------------------------:|:-------------------------:

![](assets/BSDS300.gif)  |  ![](assets/BSDS500.gif)     | ![](assets/T91.gif) 

**Set5**                    |  **Set14**                |   **Historical**

:-------------------------:|:-------------------------:|:-------------------------:

![](assets/Set5.gif)  |  ![](assets/Set14.gif)          | ![](assets/Historical.gif) 

## Losses

* **Perceptual Loss (VGG16)**

## Metrics

* **Mean Squared Error (MSE)**

* **Mean Absolute Error (MAE)**

* **Peak-Signal-Noise-Ratio (PSNR)**

## Examples

```

$ cd examples

```

* **[Get up and benchmarking quickly with PyTorch Lightning](examples/pytorch_lightning_example.py)**

* **[Coming from Keras? Try our example using the Poutyne library](examples/poutyne_example.py)**

## Running Tests

```

$ pytest -ra

```

## Cite

Please cite this repository if you used this code in your own work:

```

@software{isaac_corley_2020_3739368,

  author       = {Isaac Corley},

  title        = {PyTorch Enhance},

  month        = apr,

  year         = 2020,

  publisher    = {Zenodo},

  version      = {0.1.2},

  doi          = {10.5281/zenodo.3739368},

  url          = {https://doi.org/10.5281/zenodo.3739368}

}

```