https://github.com/coloquinte/torchsr

Super Resolution datasets and models in Pytorch
https://github.com/coloquinte/torchsr
datasets edsr pytorch rcan rdn superresolution torchvision vdsr
Last synced: 6 months ago
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Super Resolution datasets and models in Pytorch
Host: GitHub
URL: https://github.com/coloquinte/torchsr
Owner: Coloquinte
License: mit
Created: 2021-03-04T17:49:52.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2023-12-23T13:08:59.000Z (almost 2 years ago)
Last Synced: 2025-03-29T01:14:04.208Z (6 months ago)
Topics: datasets, edsr, pytorch, rcan, rdn, superresolution, torchvision, vdsr
Language: Python
Homepage:
Size: 445 KB
Stars: 199
Watchers: 7
Forks: 19
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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# Super-Resolution Networks for Pytorch

[Super-resolution](https://en.wikipedia.org/wiki/Super-resolution_imaging) 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:

![Pixelated image of a butterfly](https://raw.githubusercontent.com/Coloquinte/torchSR/v1.0.2/doc/example_small.png "Low resolution image")

![Smooth magnified image](https://raw.githubusercontent.com/Coloquinte/torchSR/v1.0.2/doc/example_x4.png "Magnified x4")

![High resolution image](https://raw.githubusercontent.com/Coloquinte/torchSR/v1.0.2/doc/example_hr.png "High resolution image")

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](https://arxiv.org/abs/1707.02921)

*   [CARN](https://arxiv.org/abs/1803.08664)

*   [RDN](https://arxiv.org/abs/1802.08797)

*   [RCAN](https://arxiv.org/abs/1807.02758)

*   [NinaSR](doc/NinaSR.md)

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.

Expand benchmark results

Set5 results

|  Network            | Parameters (M) | 2x (PSNR/SSIM) | 3x (PSNR/SSIM) | 4x (PSNR/SSIM) |

| ------------------- | -------------- | -------------- | -------------- | -------------- |

| carn                | 1.59           | 37.88 / 0.9600 | 34.32 / 0.9265 | 32.14 / 0.8942 |

| carn\_m             | 0.41           | 37.68 / 0.9594 | 34.06 / 0.9247 | 31.88 / 0.8907 |

| edsr\_baseline      | 1.37           | 37.98 / 0.9604 | 34.37 / 0.9270 | 32.09 / 0.8936 |

| edsr                | 40.7           | 38.19 / 0.9609 | 34.68 / 0.9293 | 32.48 / 0.8985 |

| ninasr\_b0          | 0.10           | 37.72 / 0.9594 | 33.96 / 0.9234 | 31.77 / 0.8877 |

| ninasr\_b1          | 1.02           | 38.14 / 0.9609 | 34.48 / 0.9277 | 32.28 / 0.8955 |

| ninasr\_b2          | 10.0           | 38.21 / 0.9612 | 34.61 / 0.9288 | 32.45 / 0.8973 |

| rcan                | 15.4           | 38.27 / 0.9614 | 34.76 / 0.9299 | 32.64 / 0.9000 |

| rdn                 | 22.1           | 38.12 / 0.9609 | 33.98 / 0.9234 | 32.35 / 0.8968 |

Set14 results

|  Network            | Parameters (M) | 2x (PSNR/SSIM) | 3x (PSNR/SSIM) | 4x (PSNR/SSIM) |

| ------------------- | -------------- | -------------- | -------------- | -------------- |

| carn                | 1.59           | 33.57 / 0.9173 | 30.30 / 0.8412 | 28.61 / 0.7806 |

| carn\_m             | 0.41           | 33.30 / 0.9151 | 30.10 / 0.8374 | 28.42 / 0.7764 |

| edsr\_baseline      | 1.37           | 33.57 / 0.9174 | 30.28 / 0.8414 | 28.58 / 0.7804 |

| edsr                | 40.7           | 33.95 / 0.9201 | 30.53 / 0.8464 | 28.81 / 0.7872 |

| ninasr\_b0          | 0.10           | 33.24 / 0.9144 | 30.02 / 0.8355 | 28.28 / 0.7727 |

| ninasr\_b1          | 1.02           | 33.71 / 0.9189 | 30.41 / 0.8437 | 28.71 / 0.7840 |

| ninasr\_b2          | 10.0           | 34.00 / 0.9206 | 30.53 / 0.8461 | 28.80 / 0.7863 |

| rcan                | 15.4           | 34.13 / 0.9216 | 30.63 / 0.8475 | 28.85 / 0.7878 |

| rdn                 | 22.1           | 33.71 / 0.9182 | 30.07 / 0.8373 | 28.72 / 0.7846 |

DIV2K results (validation set)

|  Network            | Parameters (M) | 2x (PSNR/SSIM) | 3x (PSNR/SSIM) | 4x (PSNR/SSIM) | 8x (PSNR/SSIM) |

| ------------------- | -------------- | -------------- | -------------- | -------------- | -------------- |

| carn                | 1.59           | 36.08 / 0.9451 | 32.37 / 0.8871 | 30.43 / 0.8366 | N/A            |

| carn\_m             | 0.41           | 35.76 / 0.9429 | 32.09 / 0.8827 | 30.18 / 0.8313 | N/A            |

| edsr\_baseline      | 1.37           | 36.13 / 0.9455 | 32.41 / 0.8878 | 30.43 / 0.8370 | N/A            |

| edsr                | 40.7           | 36.56 / 0.9485 | 32.75 / 0.8933 | 30.73 / 0.8445 | N/A            |

| ninasr\_b0          | 0.10           | 35.77 / 0.9428 | 32.06 / 0.8818 | 30.09 / 0.8293 | 26.60 / 0.7084 |

| ninasr\_b1          | 1.02           | 36.35 / 0.9471 | 32.51 / 0.8892 | 30.56 / 0.8405 | 26.96 / 0.7207 |

| ninasr\_b2          | 10.0           | 36.52 / 0.9482 | 32.73 / 0.8926 | 30.73 / 0.8437 | 27.07 / 0.7246 |

| rcan                | 15.4           | 36.61 / 0.9489 | 32.78 / 0.8935 | 30.73 / 0.8447 | 27.17 / 0.7292 |

| rdn                 | 22.1           | 36.32 / 0.9468 | 32.04 / 0.8822 | 30.61 / 0.8414 | N/A            |

B100 results

|  Network            | Parameters (M) | 2x (PSNR/SSIM) | 3x (PSNR/SSIM) | 4x (PSNR/SSIM) |

| ------------------- | -------------- | -------------- | -------------- | -------------- |

| carn                | 1.59           | 32.12 / 0.8986 | 29.07 / 0.8042 | 27.58 / 0.7355 |

| carn\_m             | 0.41           | 31.97 / 0.8971 | 28.94 / 0.8010 | 27.45 / 0.7312 |

| edsr\_baseline      | 1.37           | 32.15 / 0.8993 | 29.08 / 0.8051 | 27.56 / 0.7354 |

| edsr                | 40.7           | 32.35 / 0.9019 | 29.26 / 0.8096 | 27.72 / 0.7419 |

| ninasr\_b0          | 0.10           | 31.97 / 0.8974 | 28.90 / 0.8000 | 27.36 / 0.7290 |

| ninasr\_b1          | 1.02           | 32.24 / 0.9004 | 29.13 / 0.8061 | 27.62 / 0.7377 |

| ninasr\_b2          | 10.0           | 32.32 / 0.9014 | 29.23 / 0.8087 | 27.71 / 0.7407 |

| rcan                | 15.4           | 32.39 / 0.9024 | 29.30 / 0.8106 | 27.74 / 0.7429 |

| rdn                 | 22.1           | 32.25 / 0.9006 | 28.90 / 0.8004 | 27.66 / 0.7388 |

Urban100 results

|  Network            | Parameters (M) | 2x (PSNR/SSIM) | 3x (PSNR/SSIM) | 4x (PSNR/SSIM) |

| ------------------- | -------------- | -------------- | -------------- | -------------- |

| carn                | 1.59           | 31.95 / 0.9263 | 28.07 / 0.849 | 26.07 / 0.78349 |

| carn\_m             | 0.41           | 31.30 / 0.9200 | 27.57 / 0.839 | 25.64 / 0.76961 |

| edsr\_baseline      | 1.37           | 31.98 / 0.9271 | 28.15 / 0.852 | 26.03 / 0.78424 |

| edsr                | 40.7           | 32.97 / 0.9358 | 28.81 / 0.865 | 26.65 / 0.80328 |

| ninasr\_b0          | 0.10           | 31.33 / 0.9204 | 27.48 / 0.8374 | 25.45 / 0.7645 |

| ninasr\_b1          | 1.02           | 32.48 / 0.9319 | 28.29 / 0.8555 | 26.25 / 0.7914 |

| ninasr\_b2          | 10.0           | 32.91 / 0.9354 | 28.70 / 0.8640 | 26.54 / 0.8008 |

| rcan                | 15.4           | 33.19 / 0.9372 | 29.01 / 0.868 | 26.75 / 0.80624 |

| rdn                 | 22.1           | 32.41 / 0.9310 | 27.49 / 0.838 | 26.36 / 0.79460 |

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

*   [RealSR](https://github.com/csjcai/RealSR)

*   [Flicr2K](https://github.com/limbee/NTIRE2017)

*   [REDS](https://seungjunnah.github.io/Datasets/reds)

*   [Set5](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html), [Set14](https://paperswithcode.com/dataset/set14), [B100](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/), [Urban100](https://paperswithcode.com/dataset/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

```python

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()

```

Expand more examples

```python

from torchsr.datasets import Div2K

from torchsr.models import edsr, rcan

from torchsr.models.utils import ChoppedModel, SelfEnsembleModel

from torchsr.transforms import ColorJitter, Compose, RandomCrop

# Div2K dataset, cropped to 256px, width color jitter

dataset = Div2K(

    root="./data", scale=2, download=False,

    transform=Compose([

        RandomCrop(256, scales=[1, 2]),

        ColorJitter(brightness=0.2)

    ]))

# Pretrained RCAN model, with tiling for large images

model = ChoppedModel(

    rcan(scale=2, pretrained=True), scale=2,

    chop_size=400, chop_overlap=10)

# Pretrained EDSR model, with self-ensemble method for higher quality

model = SelfEnsembleModel(edsr(scale=2, pretrained=True))

```

## 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/`.

```bash

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](https://github.com/pytorch/vision) and [EDSR](https://github.com/zhouhuanxiang/EDSR-PyTorch), whose work inspired this repository.

Some of the models available here come from [EDSR-PyTorch](https://github.com/zhouhuanxiang/EDSR-PyTorch) and [CARN-PyTorch](https://github.com/nmhkahn/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}

}

```
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