Abstract
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.
Abstract (translated)
我们提出了一种基于盲目点消除原理的新单图去噪方法，我们称之为Masked and Shuffled Blind Spot Denoising（MASH）。我们关注相关噪声在真实图像中的情况。MASH是通过对输入盲目程度（遮盖水平）与（未知）噪声相关性的仔细分析来确定的结果。此外，我们还引入了一种随机化技术来削弱噪声的局部相关性，从而进一步提高去噪效果。我们对现实世界中的嘈杂图像数据集进行广泛的实验评估。我们证明了MASH与现有自监督去噪方法的性能相当或者更好。
URL
https://arxiv.org/abs/2404.09389
