EDSR-PyTorch

This repository is a PyTorch version of the paper "Enhanced Deep Residual Networks for Single Image Super-Resolution" from CVPRW 2017. You can find the original code and more information from here.

If you find our work useful in your research or publication, please cite our work:

[1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution," 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with CVPR 2017. [PDF] [arXiv] [Slide]

@InProceedings{Lim_2017_CVPR_Workshops,
  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {July},
  year = {2017}
}

This repository provides some demo codes for reproducing all the results from the paper. (Include training scripts.)

Also, pre-trained model will be uploaded soon.

Differences with Torch version

Codes are much more compact. (Removed all unnecessary parts.)
Model sizes are smaller. (About half!)
Training requires less memory. (So that we can further increase the model size.)
Test is faster.
Python-based. (Unfortunately, this code do not follow python coding convention. Sorry for that.)

Dependencies

Python (Tested with 3.6)
PyTorch (Supports 0.2.0 ONLY)

Code

Clone this repository into any place you want.

git clone https://github.com/thstkdgus35/EDSR-PyTorch
cd EDSR-PyTorch

Quick start (Demo)

You can test our super-resolution algorithm with your own images.

Place your images in test folder. (like test/puppy.jpeg)

Then, run the provided script in code folder.

Before you run the demo, please uncomment the appropriate line in demo.sh that you want to execute.

cd code       # You are now in */EDSR-PyTorch/code
sh demo.sh

You can find the result images from experiment/test_<modelName> folder.

We provide 3 pre-trained models (baseline ONLY, not full version.) till now. You can find the model from the experiment/model folder.

Model	Scale	File Name	# ResBlocks	# Filters	# Parameters
EDSR	4	EDSR_baseline_x4.pt	16	64	1.5M
MDSR	2 + 3 + 4	MDSR_baseline.pt	16	64	3.2M
MDSR (JPEG)*	2 + 3 + 4	MDSR_baseline_jpeg.pt	16	64	3.2M

*MDSR (JPEG) even reduces the JPEG artifact in output images. However, its DIV2K validation performance is slightly lower than the original MDSR.

How to train EDSR and MDSR

You have to prepare DIV2K dataset for the training.

Detailed steps for preparing data will be updated in next time.

Training scripts are also included in demo.sh. By uncomment the appropriate line and execute the script, you can train EDSR and MDSR by yourself.

cd code       # You are now in */EDSR-PyTorch/code
sh demo.sh

EDSR requires huge amount of memory, even with PyTorch. Therefore, it is impossible to test EDSR (not baseline) in 12GB GPU for now. This problem will be fixed in next update.

Detailed guide about arguments will be updated in next time.

weigq/EDSR-PyTorch

EDSR-PyTorch

Dependencies

Code

Quick start (Demo)

How to train EDSR and MDSR