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，深度学习的进展不断将图像去噪技术推向新高度。在自监督图像去噪中，盲点网络（BSN）是最常用的方法之一。然而，现有BSN算法大多采用基于点的中心掩码，该方法在处理具有大尺度空间相关噪声的图像时效率低下。本文给出了大噪声的定义，并提出了一种多掩码策略，通过使用多个不同形状的卷积核掩码进一步打破噪声的空间相关性。此外，我们提出了一种结合多掩码策略与BSN的新型自监督图像去噪方法（MM-BSN）。我们展示了不同掩码会导致显著的性能差异，而所提出的MM-BSN能够有效融合多掩码层提取的特征，同时修复由多掩码和信息传输破坏的纹理结构。我们的MM-BSN可用于解决其他BSN方法无法高效处理的大噪声去噪问题。在公开真实世界数据集上的大量实验表明，所提出的MM-BSN在sRGB图像去噪中达到了自监督甚至非配对图像去噪方法中的最优性能，且无需任何标注工作或先验知识。代码见 https://github.com/dannie125/MM-BSN。