  Image denoising is an essential part of many image processing and computer
vision tasks due to inevitable noise corruption during image acquisition.
Traditionally, many researchers have investigated image priors for the
denoising, within the Bayesian perspective based on image properties and
statistics. Recently, deep convolutional neural networks (CNNs) have shown
great success in image denoising by incorporating large-scale synthetic
datasets. However, they both have pros and cons. While the deep CNNs are
powerful for removing the noise with known statistics, they tend to lack
flexibility and practicality for the blind and real-world noise. Moreover, they
cannot easily employ explicit priors. On the other hand, traditional
non-learning methods can involve explicit image priors, but they require
considerable computation time and cannot exploit large-scale external datasets.
In this paper, we present a CNN-based method that leverages the advantages of
both methods based on the Bayesian perspective. Concretely, we divide the blind
image denoising problem into sub-problems and conquer each inference problem
separately. As the CNN is a powerful tool for inference, our method is rooted
in CNNs and propose a novel design of network for efficient inference. With our
proposed method, we can successfully remove blind and real-world noise, with a
moderate number of parameters of universal CNN.
