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Real noisy-clean pairs on a large scale are costly and difficult to obtain.Meanwhile, supervised denoisers trained on synthetic data perform poorly inpractice. Self-supervised denoisers, which learn only from single noisy images,solve the data collection problem. However, self-supervised denoising methods,especially blindspot-driven ones, suffer sizable information loss during inputor network design. The absence of valuable information dramatically reduces theupper bound of denoising performance. In this paper, we propose a simple yetefficient approach called Blind2Unblind to overcome the information loss inblindspot-driven denoising methods. First, we introduce a global-aware maskmapper that enables global perception and accelerates training. The mask mappersamples all pixels at blind spots on denoised volumes and maps them to the samechannel, allowing the loss function to optimize all blind spots at once.Second, we propose a re-visible loss to train the denoising network and makeblind spots visible. The denoiser can learn directly from raw noise imageswithout losing information or being trapped in identity mapping. We alsotheoretically analyze the convergence of the re-visible loss. Extensiveexperiments on synthetic and real-world datasets demonstrate the superiorperformance of our approach compared to previous work. Code is available athttps://github.com/demonsjin/Blind2Unblind.

Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots