  We introduce a novel approach to single image denoising based on the Blind
Spot Denoising principle, which we call MAsked and SHuffled Blind Spot
Denoising (MASH). We focus on the case of correlated noise, which often plagues
real images. MASH is the result of a careful analysis to determine the
relationships between the level of blindness (masking) of the input and the
(unknown) noise correlation. Moreover, we introduce a shuffling technique to
weaken the local correlation of noise, which in turn yields an additional
denoising performance improvement. We evaluate MASH via extensive experiments
on real-world noisy image datasets. We demonstrate on par or better results
compared to existing self-supervised denoising methods.
