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Fast and flexible processing are two essential requirements for a number ofpractical applications of image denoising. Current state-of-the-art methods,however, still require either high computational cost or limited scopes of thetarget. We introduce an efficient ensemble network trained via a competition ofexpert networks, as an application for image blind denoising. We realizeautomatic division of unlabeled noisy datasets into clusters respectivelyoptimized to enhance denoising performance. The architecture is scalable, canbe extended to deal with diverse noise sources/levels without increasing thecomputation time. Taking advantage of this method, we save up to approximately90% of computational cost without sacrifice of the denoising performancecompared to single network models with identical architectures. We also comparethe proposed method with several existing algorithms and observe significantoutperformance over prior arts in terms of computational efficiency.

Fast and Flexible Image Blind Denoising via Competition of Experts