  Recently, numerous studies have been conducted on supervised learning-based
image denoising methods. However, these methods rely on large-scale noisy-clean
image pairs, which are difficult to obtain in practice. Denoising methods with
self-supervised training that can be trained with only noisy images have been
proposed to address the limitation. These methods are based on the
convolutional neural network (CNN) and have shown promising performance.
However, CNN-based methods do not consider using nonlocal self-similarities
essential in the traditional method, which can cause performance limitations.
This paper presents self-similarity attention (SS-Attention), a novel
self-attention module that can capture nonlocal self-similarities to solve the
problem. We focus on designing a lightweight self-attention module in a
pixel-wise manner, which is nearly impossible to implement using the classic
self-attention module due to the quadratically increasing complexity with
spatial resolution. Furthermore, we integrate SS-Attention into the blind-spot
network called self-similarity-based blind-spot network (SS-BSN). We conduct
the experiments on real-world image denoising tasks. The proposed method
quantitatively and qualitatively outperforms state-of-the-art methods in
self-supervised denoising on the Smartphone Image Denoising Dataset (SIDD) and
Darmstadt Noise Dataset (DND) benchmark datasets.
