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.
Test is faster.
Python-based.

Update log

Jan 04, 2018
- Many parts are re-written. You cannot use previous scripts and models directly.
- Pre-trained MDSR is temporarily disabled.
- Training details are included.
Jan 09, 2018
- Missing files are included (code/data/MyImage.py).
- Some links are fixed.
Jan 16, 2018
- Memory efficient forward function is implemented.
- Add --chop_forward argument to your script to enable it.
- Basically, this function first split a large image to small patches. Those images are merged after super-resolution. I checked that this function works well with 12GB memory, 4000 x 2000 input image in scale 4. (Therefore, the output will be 16000 x 8000.)

Dependencies

Python (Tested with 3.6)
PyTorch >= 0.2.0

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

We used DIV2K dataset for training. Please download it from here (7.1GB).

Unpack the tar file to any place you want. Then, change the dir_data argument in code/option.py to the place where you unpack DIV2K images.

We recommend you to pre-process the images before training. This step will decode and collect all png files into one huge binary file. Use code/tools/png2binary.py for this process.

If you do not have enough RAM (>= 16GB), change the ext argument in code/option.py to png. However, each image in DIV2K is so large that disk access and decoding png files can be a bottleneck.

Training scripts are also included in demo.sh. By uncommenting the appropriate line and executing the script, you can train EDSR and MDSR by yourself. Note that EDSR (x3, x4) requires pre-trained EDSR (x2). By removing --pre_train argument in the provided script, you can ignore this constraint.

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

yangcha/EDSR-PyTorch

EDSR-PyTorch

Dependencies

Code

Quick start (Demo)

How to train EDSR and MDSR