Fast and Flexible Image Blind Denoising via Competition of Experts
This addresses the need for fast and flexible denoising in practical applications, though it appears incremental as it builds on existing ensemble and clustering ideas.
The paper tackles the problem of high computational cost and limited flexibility in image blind denoising by introducing an efficient ensemble network trained via competition of experts, which saves up to 90% of computational cost without sacrificing performance compared to single network models.
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 prior arts in terms of computational efficiency.