When AWGN-based Denoiser Meets Real Noises

Understand

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 real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN.