This project is the implementation of paper "Xue S. et al., Wavelet-based Residual Attention Network for Image Super-Resolution, Neurocomputing, 2019".
- OS: CentOS 7 Linux kernel 3.10.0-514.el7.x86_64
- CPU: Intel Xeon(R) CPU E5-2667 v4 @ 3.20GHz x 32
- Memory: 251.4 GB
- GPU: NVIDIA Tesla P4, 8 GB
- Python 2.7.14
- Tensorflow 1.13
- Keras
These datasets are the same as other paper provided. Readers can directly use them or download them from here:
BSDS100, BSDS200, General-100, Set5, Set14, T91, Train_291, Urban100, and DIV2K.
python main.py- scale = 2/3/4
- depth = 8
- ratio = 4
- width = 64
- alpha = 0.1
- batch_size = 64
- epochs = 200
python predict.pyPh.D. candidate, Shengke Xue
College of Information Science and Electronic Engineering
Zhejiang University, Hangzhou, P.R. China
Email: [email protected], [email protected]