Fast and Flexible Image Blind Denoising via Competition of Experts

Shunta Maeda; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 528-529

Fast and flexible processing are two essential requirements for a number of practical applications of image denoising. Current state-of-the-art methods, however, still require either high computational cost or limited scopes of the target. We introduce an efficient ensemble network trained via a competition of expert networks, as an application for image blind denoising. We realize automatic division of unlabeled noisy datasets into clusters respectively optimized to enhance denoising performance. The architecture is scalable, can be extended to deal with diverse noise sources/levels without increasing the computation time. Taking advantage of this method, we save up to approximately 90% of computational cost without sacrifice of the denoising performance compared to single network models with identical architectures. We also compare the proposed method with several existing algorithms and observe significant outperformance over the standard denoising algorithm BM3D in terms of computational efficiency.