Super-Resolution Networks for Pytorch

Super-resolution is a process that increases the resolution of an image, adding additional details. Methods using neural networks give the most accurate results, much better than other interpolation methods. With the right training, it is even possible to make photo-realistic images.

For example, here is a low-resolution image, magnified x4 by a neural network, and a high resolution image of the same object:

In this repository, you will find:

• the popular super-resolution networks, pretrained
• common super-resolution datasets
• Pytorch datasets and transforms adapted to super-resolution
• a unified training script for all models

Models

The following pretrained models are available. Click on the links for the paper:

• EDSR
• CARN
• RDN
• RCAN
• NinaSR

Newer and larger models perform better: the most accurate models are EDSR (huge), RCAN and NinaSR-B2. For practical applications, I recommend a smaller model, such as NinaSR-B1.

All models are defined in torchsr.models. Other useful tools to augment your models, such as self-ensemble methods and tiling, are present in torchsr.models.utils.

Datasets

The following datasets are available. Click on the links for the project page:

• DIV2K
• RealSR
• Flicr2K
• REDS
• Set5, Set14, B100, Urban100

All datasets are defined in torchsr.datasets. They return a list of images, with the high-resolution image followed by downscaled or degraded versions. Data augmentation methods are provided in torchsr.transforms.

Datasets are downloaded automatically when using the download=True flag, or by running the corresponding script i.e. ./scripts/download_div2k.sh.

Usage

from torchsr.datasets import Div2K
from torchsr.models import ninasr_b0
from torchvision.transforms.functional import to_pil_image, to_tensor

# Div2K dataset
dataset = Div2K(root="./data", scale=2, download=False)

# Get the first image in the dataset (High-Res and Low-Res)
hr, lr = dataset[0]

# Download a pretrained NinaSR model
model = ninasr_b0(scale=2, pretrained=True)

# Run the Super-Resolution model
lr_t = to_tensor(lr).unsqueeze(0)
sr_t = model(lr_t)
sr = to_pil_image(sr_t.squeeze(0).clamp(0, 1))
sr.show()

Training

A script is available to train the models from scratch, evaluate them, and much more. It is not part of the pip package, and requires additional dependencies. More examples are available in scripts/.

pip install piq tqdm tensorboard  # Additional dependencies
python -m torchsr.train -h
python -m torchsr.train --arch edsr_baseline --scale 2 --download-pretrained --images test/butterfly.png --destination results/
python -m torchsr.train --arch edsr_baseline --scale 2 --download-pretrained --validation-only
python -m torchsr.train --arch edsr_baseline --scale 2 --epochs 300 --loss l1 --dataset-train div2k_bicubic

You can evaluate models from the command line as well. For example, for EDSR with the paper's PSNR evaluation:

python -m torchsr.train --validation-only --arch edsr_baseline --scale 2 --dataset-val set5 --chop-size 400 --download-pretrained --shave-border 2 --eval-luminance

Acknowledgements

Thanks to the people behind torchvision and EDSR, whose work inspired this repository. Some of the models available here come from EDSR-PyTorch and CARN-PyTorch.

To cite this work, please use:

@misc{torchsr,
  author = {Gabriel Gouvine},
  title = {Super-Resolution Networks for Pytorch},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Coloquinte/torchSR}},
  doi = {10.5281/zenodo.4868308}
}

@misc{ninasr,
  author = {Gabriel Gouvine},
  title = {NinaSR: Efficient Small and Large ConvNets for Super-Resolution},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Coloquinte/torchSR/blob/main/doc/NinaSR.md}},
  doi = {10.5281/zenodo.4868308}
}