When smartphone cameras are used to take photos of digital screens, usually
moire patterns result, severely degrading photo quality. In this paper, we
design a wavelet-based dual-branch network (WDNet) with a spatial attention
mechanism for image demoireing. Existing image restoration methods working in
the RGB domain have difficulty in distinguishing moire patterns from true scene
texture. Unlike these methods, our network removes moire patterns in the
wavelet domain to separate the frequencies of moire patterns from the image
content. The network combines dense convolution modules and dilated convolution
modules supporting large receptive fields. Extensive experiments demonstrate
the effectiveness of our method, and we further show that WDNet generalizes to
removing moire artifacts on non-screen images. Although designed for image
demoireing, WDNet has been applied to two other low-levelvision tasks,
outperforming state-of-the-art image deraining and derain-drop methods on the
Rain100h and Raindrop800 data sets, respectively.

本文设计了基于小波变换和空间注意力机制的双分支网络（WDNet），用于图像去马赛克处理，并在 Rain 100 和 Raindrop 800 数据集上优于现有的图像去雨滴方法。