Towards Interactive Self-Supervised Denoising (TCSVT 2023) Paper
Mingde Yao, Dongliang He, Xin Li, Fu Li, and Zhiwei Xiong
University of Science and Technology of China (USTC)
🚀 This is the official repository of Interactive_Self_supervised_Denoising (TCSVT 2023).
Our approach allows users to select the denoising output that well suits their perceptual requirements, making it a more subjective and personalized experience.
We provide the training and test code along with the trained weights and the dataset (train+test) used for this work. If you find this repository useful, please ⭐star this repo and 📄cite our paper .
Reference:
Mingde Yao, Dongliang He, Xin Li, Fu Li, and Zhiwei Xiong, "Towards Interactive Self-Supervised Denoising", IEEE Transactions on Transactions on Circuits and Systems for Video Technology, 2023.
Bibtex:
@article{yao2023towards,
title={Towards interactive self-supervised denoising},
author={Yao, Mingde and He, Dongliang and Li, Xin and Li, Fu and Xiong, Zhiwei},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
year={2023},
publisher={IEEE}
}
We have two stages for training.
Please run the train.py for training and test.py for testing.
More details will be updated recently. If you have more questions, please contact me.
If you have any problem with the released code, please do not hesitate to open an issue.
For any inquiries or questions, please contact me by email ([email protected]).