  Real noisy-clean pairs on a large scale are costly and difficult to obtain.
Meanwhile, supervised denoisers trained on synthetic data perform poorly in
practice. 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 input
or network design. The absence of valuable information dramatically reduces the
upper bound of denoising performance. In this paper, we propose a simple yet
efficient approach called Blind2Unblind to overcome the information loss in
blindspot-driven denoising methods. First, we introduce a global-aware mask
mapper that enables global perception and accelerates training. The mask mapper
samples all pixels at blind spots on denoised volumes and maps them to the same
channel, allowing the loss function to optimize all blind spots at once.
Second, we propose a re-visible loss to train the denoising network and make
blind spots visible. The denoiser can learn directly from raw noise images
without losing information or being trapped in identity mapping. We also
theoretically analyze the convergence of the re-visible loss. Extensive
experiments on synthetic and real-world datasets demonstrate the superior
performance of our approach compared to previous work. Code is available at
https://github.com/demonsjin/Blind2Unblind.
