  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.
