  Deep convolutional neural networks (CNNs) are used for image denoising via
automatically mining accurate structure information. However, most of existing
CNNs depend on enlarging depth of designed networks to obtain better denoising
performance, which may cause training difficulty. In this paper, we propose a
multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three
stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet
transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a
dynamic convolution to dynamically adjust parameters of several convolutions
for making a tradeoff between denoising performance and computational costs.
WEB uses a combination of signal processing technique (i.e., wavelet
transformation) and discriminative learning to suppress noise for recovering
more detailed information in image denoising. To further remove redundant
features, RB is used to refine obtained features for improving denoising
effects and reconstruct clean images via improved residual dense architectures.
Experimental results show that the proposed MWDCNN outperforms some popular
denoising methods in terms of quantitative and qualitative analysis. Codes are
available at https://github.com/hellloxiaotian/MWDCNN.
