pytorch-enhance: Image Super-Resolution in PyTorch

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

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
VDSR from Lee et al. Accurate Image Super-Resolution Using Very Deep Convolutional Networks
ESPCN from Shi et. al Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
SRResNet from Ledig et. al Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
EDSR from Lim et. al Enhanced Deep Residual Networks for Single Image Super-Resolution

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

Datasets

The following benchmark datasets are available for usage:

BSDS100
BSDS200
BSDS300
BSDS500
Set5
Set14
T91
Historical
Urban100
Manga109
General100
DIV2K

Dataset Samples

BSDS300	BSDS500	T91

Set5	Set14	Historical

Losses

Perceptual Loss (VGG16)

Metrics

Mean Squared Error (MSE)
Mean Absolute Error (MAE)
Peak-Signal-Noise-Ratio (PSNR)

Examples

$ cd examples

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}
}

License

alanderex/pytorch-enhance

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