@inproceedings{
quan2024pseudosiamese,
title={Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising},
author={Yuhui Quan and Tianxiang Zheng and Hui Ji},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=O3nPufVaee}
}
Real-world image denoising remains a challenge task. This paper studies self-supervised image denoising, requiring only noisy images captured in a single shot. We revamping the blind-spot technique by leveraging the transformer’s capability for long-range pixel interactions, which is crucial for effectively removing noise dependence in relating pixel–a requirement for achieving great performance for the blind-spot technique. The proposed method integrates these elements with two key innovations: a directional self-attention (DSA) module using a half-plane grid for self-attention, creating a sophisticated blind-spot structure, and a Siamese architecture with mutual learning to mitigate the performance impacts
from the restricted attention grid in DSA. Experiments on benchmark datasets demonstrate that our method outperforms existing self-supervised and clean-image-free methods. This combination of blind-spot and transformer techniques provides a natural synergy for tackling real-world image denoising challenges.
Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising
Yuhui Quan Tianxiang Zheng Hui Ji