  Convolutional neural networks (CNNs) have shown outstanding performance on
image denoising with the help of large-scale datasets. Earlier methods naively
trained a single CNN with many pairs of clean-noisy images. However, the
conditional distribution of the clean image given a noisy one is too
complicated and diverse, so that a single CNN cannot well learn such
distributions. Therefore, there have also been some methods that exploit
additional noise level parameters or train a separate CNN for a specific noise
level parameter. These methods separate the original problem into easier
sub-problems and thus have shown improved performance than the naively trained
CNN. In this step, we raise two questions. The first one is whether it is an
optimal approach to relate the conditional distribution only to noise level
parameters. The second is what if we do not have noise level information, such
as in a real-world scenario. To answer the questions and provide a better
solution, we propose a novel Bayesian framework based on the variational
approximation of objective functions. This enables us to separate the
complicated target distribution into simpler sub-distributions. Eventually, the
denoising CNN can conquer noise from each sub-distribution, which is generally
an easier problem than the original. Experiments show that the proposed method
provides remarkable performance on additive white Gaussian noise (AWGN) and
real-noise denoising while requiring fewer parameters than recent
state-of-the-art denoisers.
