  The tradeoff between receptive field size and efficiency is a crucial issue
in low level vision. Plain convolutional networks (CNNs) generally enlarge the
receptive field at the expense of computational cost. Recently, dilated
filtering has been adopted to address this issue. But it suffers from gridding
effect, and the resulting receptive field is only a sparse sampling of input
image with checkerboard patterns. In this paper, we present a novel multi-level
wavelet CNN (MWCNN) model for better tradeoff between receptive field size and
computational efficiency. With the modified U-Net architecture, wavelet
transform is introduced to reduce the size of feature maps in the contracting
subnetwork. Furthermore, another convolutional layer is further used to
decrease the channels of feature maps. In the expanding subnetwork, inverse
wavelet transform is then deployed to reconstruct the high resolution feature
maps. Our MWCNN can also be explained as the generalization of dilated
filtering and subsampling, and can be applied to many image restoration tasks.
The experimental results clearly show the effectiveness of MWCNN for image
denoising, single image super-resolution, and JPEG image artifacts removal.
