@article{Zhou_Jiao_Huang_Wang_Huang_2019, title={Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5179}, DOI={10.1609/aaai.v33i01.330110085}, abstractNote={<p>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.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhou, Yuqian and Jiao, Jianbo and Huang, Haibin and Wang, Jue and Huang, Thomas}, year={2019}, month={Jul.}, pages={10085-10086} }