FSR: A General Frequency-Oriented Framework to Accelerate Image Super-resolution Networks

This is the PyTorch implementation of paper: FSR (AAAI 2023 Oral).

Framework

Dependencies

• Python >= 3.6 (Recommend to use Anaconda)
• PyTorch >= 1.5.0
• NVIDIA GPU + CUDA
• Python packages: pip install numpy opencv-python lmdb
• [option] Python packages: pip install tensorboardX, for visualizing curves.

Codes

• Our codes version based on BasicSR.

Dataset

• All datasets drawn from the existing literature are publicly available. (DIV8K, TEST2K, TEST4K, TEST8K).

Get Started

train

cd codes
python train.py --whichModule fsrcnn 
# whichModule, choices=['fsrcnn','carn','srresnet','rcan']

test

cd codes
python test.py --whichModule fsrcnn 
# whichModule, choices=['fsrcnn','carn','srresnet','rcan']

Result

Acknowledgement

• The code is based on BasicSR, with reference of ClassSR.

Contact

• If you have any questions, please contact ljm22@mails.tsinghua.edu.cn.