  In computer vision, convolutional networks (CNNs) often adopts pooling to
enlarge receptive field which has the advantage of low computational
complexity. However, pooling can cause information loss and thus is detrimental
to further operations such as features extraction and analysis. Recently,
dilated filter has been proposed to trade off between receptive field size and
efficiency. But the accompanying gridding effect can cause a sparse sampling of
input images with checkerboard patterns. To address this problem, in this
paper, we propose a novel multi-level wavelet CNN (MWCNN) model to achieve
better trade-off between receptive field size and computational efficiency. The
core idea is to embed wavelet transform into CNN architecture to reduce the
resolution of feature maps while at the same time, increasing receptive field.
Specifically, MWCNN for image restoration is based on U-Net architecture, and
inverse wavelet transform (IWT) is deployed to reconstruct the high resolution
(HR) feature maps. The proposed MWCNN can also be viewed as an improvement of
dilated filter and a generalization of average pooling, and can be applied to
not only image restoration tasks, but also any CNNs requiring a pooling
operation. The experimental results demonstrate effectiveness of the proposed
MWCNN for tasks such as image denoising, single image super-resolution, JPEG
image artifacts removal and object classification.
