Awesome
RealSRT
PyTorch implementation of ECCV 2024 paper "Confidence-Based Iterative Generation for Real-World Image Super-Resolution"
Introduction
<div align=center> <img src="./figures/teaser.png" width="70%" height="70%">Visualizations of our confidence-based iterative generation process for real-world SR.
</div>Method
<div align=center> <img src="./figures/pipeline.png">Overview of RealSRT.
</div>Installation
This implementation is based on BasicSR
git clone https://github.com/USTC-JialunPeng/RealSRT
cd RealSRT
pip install -r requirements.txt
python setup.py develop
Inference
-
Download the pre-trained model and place it in
./experiments/pretrained_models/
-
Download the test dataset (e.g., RealSR), place input images in
/data/input/
and place target images (if available) in/data/target/
-
Testing
python inference_realsrt.py --input /data/input/ --output /data/results/ --model_path experiments/pretrained_models/net_g_80000.pth
- To reproduce scores in Table 1, run
python calculate_metrics.py
Citing
If our method is useful for your research, please consider citing.
@inproceedings{peng2024confidence,
title={Confidence-Based Iterative Generation for Real-World Image Super-Resolution},
author={Peng, Jialun and Luo, Xin and Fu, Jingjing and Liu, Dong},
booktitle={European Conference on Computer Vision},
year={2024}
}