  We tackle a challenging blind image denoising problem, in which only single
distinct noisy images are available for training a denoiser, and no information
about noise is known, except for it being zero-mean, additive, and independent
of the clean image. In such a setting, which often occurs in practice, it is
not possible to train a denoiser with the standard discriminative training or
with the recently developed Noise2Noise (N2N) training; the former requires the
underlying clean image for the given noisy image, and the latter requires two
independently realized noisy image pair for a clean image. To that end, we
propose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise)
method that first learns a generative model that can 1) simulate the noise in
the given noisy images and 2) generate a rough, noisy estimates of the clean
images, then 3) iteratively trains a denoiser with subsequently synthesized
noisy image pairs (as in N2N), obtained from the generative model. In results,
we show the denoiser trained with our GAN2GAN achieves an impressive denoising
performance on both synthetic and real-world datasets for the blind denoising
setting; it almost approaches the performance of the standard
discriminatively-trained or N2N-trained models that have more information than
ours, and it significantly outperforms the recent baseline for the same
setting, \textit{e.g.}, Noise2Void, and a more conventional yet strong one,
BM3D. The official code of our method is available at
https://github.com/csm9493/GAN2GAN.
