  Deep neural networks have been widely used in image denoising during the past
few years. Even though they achieve great success on this problem, they are
computationally inefficient which makes them inappropriate to be implemented in
mobile devices. In this paper, we propose an efficient deep neural network for
image denoising based on pixel-wise classification. Despite using a
computationally efficient network cannot effectively remove the noises from any
content, it is still capable to denoise from a specific type of pattern or
texture. The proposed method follows such a divide and conquer scheme. We first
use an efficient U-net to pixel-wisely classify pixels in the noisy image based
on the local gradient statistics. Then we replace part of the convolution
layers in existing denoising networks by the proposed Class Specific
Convolution layers (CSConv) which use different weights for different classes
of pixels. Quantitative and qualitative evaluations on public datasets
demonstrate that the proposed method can reduce the computational costs without
sacrificing the performance compared to state-of-the-art algorithms.
