Awesome
PDASR
Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
Yuehan Zhang, Bo Ji, Jia Hao, and Angela Yao
In ECCV 2022
Introduction
Single Image Super-Resolution (SISR) usually only does well in either objective quality or perceptual quality, due to the perception-distortion trade-off. In this paper, we proposed a two-stage model trained with low-frequency constraint and designed ADMM algorithm. Experimentally. our method achieve high perfromance in both PSNR/SSIM (objective quality) and NRQM/LPIPS (perceptual quality). Check followings for details.
Paper | Sumpplementary Material
Getting Start
- clone this repository
git clone https://github.com/Yuehan717/PDASR
cd PDASR/src
- Install dependencies. (Python >= 3.7 + CUDA)
- Require pytorch=1.9.1: official instructions
- Install other requirements
pip install -r requirements.txt
Data Preparation
- Download testing data from Google Drive
- Put data under folder or change the dir value in
(Temporally does not support to test self-collected data)
Testing
- Download trained model and put it under the folder models.
- Run following command
python test.py --scale 4 --save test_results --templateD HAN --templateP Clique \
--dir_data [root of testing sets] --data_test Set5+Set14+B100+Urban100 \
--pre_train ../models/model_trained.pt --save_results
We also provide the testing results in our paper.
Training
Instructions coming soon
Our code is based on EDSR. Thanks to their great work.