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.

我们介绍了一种新颖的基于盲点去噪原理的单图像去噪方法，称为MASH。我们关注相关噪声的情况，该问题常常困扰真实图像。MASH是一种仔细分析的结果，用于确定输入的盲度级别（遮挡）与（未知）噪声相关性之间的关系。此外，我们引入了一种洗牌技术，用于削弱噪声的局部相关性，从而获得额外的去噪性能改进。我们通过对真实世界噪声图像数据集进行广泛实验来评估MASH。与现有的自监督去噪方法相比，我们展示了相当或更好的结果。

真实世界图像的遮蔽和随机化暗点降噪