ERCAN for NTIRE2019-Super-Resolution

PyTorch implementation of "Enhanced Residual Attention Network for Single Image Super-Resolution".

Code&Model url: https://drive.google.com/open?id=17MEe5NrpmZZAkO_YFREUFjPAeB9XfEGf

The code is built on EDSR (PyTorch) and tested on Ubuntu 16.04 environment.

Contribution

Main Contributions the proposed method:

Low-resolution images downsampled to half(2x) for sharper edge and more detail information. Input small images can also increase the speed of the network reconstructed image.
Introducing the idea of multi-scale channel attention into our model, compared to the previous channel attention mechanism, our method can learn a richer inter-channel relationship, thereby improving network performance.
Employing self-ensemble, RGB sub-mean to improve the quality of the reconstructed image.

Dependencies

Python 3
PyTorch >= 0.4.0
numpy
skimage
imageio
matplotlib
tqdm

Train

Prepare training data

Download NTIRE2019 training data (60 training + 20 validation images)
Downsize(2x) these training data by bicubic (We already prepare these images in our source code)
Specify '--dir_data' based on the HR and LR images path. In option.py, '--ext' is set as 'sep_reset', which first convert .png to .npy. If all the training images (.png) are converted to .npy files, then set '--ext sep' to skip converting files.

For more informaiton, please refer to EDSR(PyTorch).

Begin to train

   # Train our ERCAN model
   cd src
   python3 main.py --model ERCAN --save ERCAN

Test(Quick start)

   # Test our trained ERCAN model
   cd to src
   sh demo.sh

Architecture

Network Architecture

Multi-scale Attention Module

Results

References :

@InProceedings{EDSR,
  author    = {Bee Lim and Sanghyun Son},
  title     = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {CVPR Workshops},
  year      = {2017}
}

@InProceedings{RCAN,
  author    = {Yulun Zhang and Kunpeng Li},
  title     = {Image Super-Resolution Using Very Deep Residual Channel Attention Networks},
  booktitle = {ECCV},
  year      = {2018}
}

@InProceedings{SKN,
  author    = {Xiang Li and Wenhai Wang},
  title     = {Selective Kernel Networks},
  booktitle = {CVPR},
  year      = {2019}
}

BaiFree/ERCAN