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
this https URL

本文提出一种利用多种掩码策略和 BSN 相结合的多掩膜策略（MM-BSN）来解决大噪声去噪问题的自监督图像去噪方法，实验结果表明该方法在 sRGB 图像去噪方面表现出良好的性能并且是目前自监督和非配对图像去噪方法中表现最好的。