Recent advances in deep learning have been pushing image denoising techniques to a new level. In self-supervised image denoising, blind-spot network (BSN) is one of the most common methods. However, most of the existing BSN algorithms use a dot-based central mask, which is recognized as inefficient for images with large-scale spatially correlated noise. In this paper, we give the definition of large-noise and propose a multi-mask strategy using multiple convolutional kernels masked in different shapes to further break the noise spatial correlation. Furthermore, we propose a novel self-supervised image denoising method that combines the multi-mask strategy with BSN (MM-BSN). We show that different masks can cause significant performance differences, and the proposed MM-BSN can efficiently fuse the features extracted by multi-masked layers, while recovering the texture structures destroyed by multi-masking and information transmission. Our MM-BSN can be used to address the problem of large-noise denoising, which cannot be efficiently handled by other BSN methods. Extensive experiments on public real-world datasets demonstrate that the proposed MM-BSN achieves state-of-the-art performance among self-supervised and even unpaired image denoising methods for sRGB images denoising, without any labelling effort or prior knowledge. Code can be found in https://github.com/dannie125/MM-BSN.

翻译：近年来，深度学习的发展将图像去噪技术推向了新高度。在自监督图像去噪领域，盲点网络（BSN）是最常用的方法之一。然而，现有BSN算法多采用基于单点的中心掩膜策略，这种策略在处理具有大规模空间相关噪声的图像时效率低下。本文首先给出大噪声的定义，并提出一种多掩膜策略——通过使用多个不同形状掩膜卷积核，进一步打破噪声的空间关联性。在此基础上，我们提出了一种融合多掩膜策略与BSN的新型自监督图像去噪方法（MM-BSN）。研究表明，不同掩膜会导致显著的性能差异，而所提出的MM-BSN能够高效融合多层掩膜提取的特征，同时修复因多掩膜和信息传输而破坏的纹理结构。该方法可有效解决其他BSN算法难以处理的大噪声去噪问题。在公开真实场景数据集上的大量实验表明，我们的MM-BSN在sRGB图像去噪任务中，无需任何标注或先验知识，即可在自监督甚至非配对图像去噪方法中达到最优性能。代码见：https://github.com/dannie125/MM-BSN。