Noisy-As-Clean: Learning Unsupervised Denoising from the Corrupted Image
Recently, supervised deep networks have achieved promising performance on image denoising, by learning image priors and noise statistics on plenty pairs of noisy and clean images. Unsupervised denoising networks are also proposed to use external noisy images for training. However, for an unseen test image, these denoising networks ignore either its particular image prior, the noise statistics, or both. That is, the networks learned from external images inherently suffer from a domain gap problem that the image priors and noise statistics are very different between the training and test images. This problem becomes more clear when dealing with the signal dependent realistic noise. To bridge this gap, in this work, we propose a novel “Noisy-As-Clean” (NAC) strategy of training unsupervised denoising networks. In NAC, the corrupted test image is taken as the “clean” target, while the inputs are simulated images consisting of the test image and similar corruptions. A simple and useful observation on our NAC is: as long as the noise is weak, it is feasible to learn an unsupervised network only with the corrupted image, approximating the optimal parameters of a supervised network learned with pairs of noisy and clean images. Experiments show that the unsupervised networks trained with our NAC strategy outperform previous networks, including supervised ones, on synthetic and realistic noise removal.