  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.
