Pseudo-Siamese Blind-spot Transformers for Self-Supervised Real-World Denoising

Yuhui Quan, Tianxiang Zheng, Hui Ji

Keywords: self-supervision, image denoising, low-level vision, transformer

TL;DR: Self-supervised blind-spot transformer for real-world image denoising

Abstract:

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.

Primary Area: Machine vision

Submission Number: 3945