Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise - Zhou, Yuqian; Jiao, Jianbo; Huang, Haibin; Wang, Jue; Huang, Thomas (Proceedings of the AAAI Conference on Artificial Intelligence, 2019)
Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches. Our preliminary results demonstrated the effectiveness of the newly-proposed noise model and adaptation strategies.