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When AWGN-based Denoiser Meets Real Noises
[article]
2019
arXiv
pre-print
Discriminative learning-based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a
arXiv:1904.03485v2
fatcat:iywtuqvyk5c5tistmdczipsuhe