Blind image denoising is an important yet very challenging problem in
computer vision due to the complicated acquisition process of real images. In
this work we propose a new variational inference method, which integrates both
noise estimation and image denoising into a unique Bayesian framework, for
blind image denoising. Specifically, an approximate posterior, parameterized by
deep neural networks, is presented by taking the intrinsic clean image and
noise variances as latent variables conditioned on the input noisy image. This
posterior provides explicit parametric forms for all its involved
hyper-parameters, and thus can be easily implemented for blind image denoising
with automatic noise estimation for the test noisy image. On one hand, as other
data-driven deep learning methods, our method, namely variational denoising
network (VDN), can perform denoising efficiently due to its explicit form of
posterior expression. On the other hand, VDN inherits the advantages of
traditional model-driven approaches, especially the good generalization
capability of generative models. VDN has good interpretability and can be
flexibly utilized to estimate and remove complicated non-i.i.d. noise collected
in real scenarios. Comprehensive experiments are performed to substantiate the
superiority of our method in blind image denoising.Comment: 11 pages, 4 figure