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About PyTorch 1.2.0

EDSR-PyTorch

About PyTorch 1.1.0

This repository is an official PyTorch implementation of the paper "Enhanced Deep Residual Networks for Single Image Super-Resolution" from CVPRW 2017, 2nd NTIRE. 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," <i>2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with CVPR 2017. </i> [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}
}

We provide scripts for reproducing all the results from our paper. You can train your model from scratch, or use a pre-trained model to enlarge your images.

Differences between Torch version

Dependencies

Code

Clone this repository into any place you want.

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

Quickstart (Demo)

You can test our super-resolution algorithm with your images. Place your images in test folder. (like test/<your_image>) We support png and jpeg files.

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

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

You can find the result images from experiment/test/results folder.

ModelScaleFile name (.pt)Parameters**PSNR
EDSR2EDSR_baseline_x21.37 M34.61 dB
*EDSR_x240.7 M35.03 dB
3EDSR_baseline_x31.55 M30.92 dB
*EDSR_x343.7 M31.26 dB
4EDSR_baseline_x41.52 M28.95 dB
*EDSR_x443.1 M29.25 dB
MDSR2MDSR_baseline3.23 M34.63 dB
*MDSR7.95 M34.92 dB
3MDSR_baseline30.94 dB
*MDSR31.22 dB
4MDSR_baseline28.97 dB
*MDSR29.24 dB

*Baseline models are in experiment/model. Please download our final models from here (542MB) **We measured PSNR using DIV2K 0801 ~ 0900, RGB channels, without self-ensemble. (scale + 2) pixels from the image boundary are ignored.

You can evaluate your models with widely-used benchmark datasets:

Set5 - Bevilacqua et al. BMVC 2012,

Set14 - Zeyde et al. LNCS 2010,

B100 - Martin et al. ICCV 2001,

Urban100 - Huang et al. CVPR 2015.

For these datasets, we first convert the result images to YCbCr color space and evaluate PSNR on the Y channel only. You can download benchmark datasets (250MB). Set --dir_data <where_benchmark_folder_located> to evaluate the EDSR and MDSR with the benchmarks.

You can download some results from here. The link contains EDSR+_baseline_x4 and EDSR+_x4. Otherwise, you can easily generate result images with demo.sh scripts.

How to train EDSR and MDSR

We used DIV2K dataset to train our model. Please download it from here (7.1GB).

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

We recommend you to pre-process the images before training. This step will decode all png files and save them as binaries. Use --ext sep_reset argument on your first run. You can skip the decoding part and use saved binaries with --ext sep argument.

If you have enough RAM (>= 32GB), you can use --ext bin argument to pack all DIV2K images in one binary file.

You can train EDSR and MDSR by yourself. All scripts are provided in the src/demo.sh. Note that EDSR (x3, x4) requires pre-trained EDSR (x2). You can ignore this constraint by removing --pre_train <x2 model> argument.

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

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