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We tackle a challenging blind image denoising problem, in which only singledistinct noisy images are available for training a denoiser, and no informationabout noise is known, except for it being zero-mean, additive, and independentof the clean image. In such a setting, which often occurs in practice, it isnot possible to train a denoiser with the standard discriminative training orwith the recently developed Noise2Noise (N2N) training; the former requires theunderlying clean image for the given noisy image, and the latter requires twoindependently realized noisy image pair for a clean image. To that end, wepropose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise)method that first learns a generative model that can 1) simulate the noise inthe given noisy images and 2) generate a rough, noisy estimates of the cleanimages, then 3) iteratively trains a denoiser with subsequently synthesizednoisy image pairs (as in N2N), obtained from the generative model. In results,we show the denoiser trained with our GAN2GAN achieves an impressive denoisingperformance on both synthetic and real-world datasets for the blind denoisingsetting; it almost approaches the performance of the standarddiscriminatively-trained or N2N-trained models that have more information thanours, and it significantly outperforms the recent baseline for the samesetting, \textit{e.g.}, Noise2Void, and a more conventional yet strong one,BM3D. The official code of our method is available athttps://github.com/csm9493/GAN2GAN.

GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images