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.

中文翻译：

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用于图像去梦的基于小波的双分支网络

当使用智能手机相机拍摄数码屏幕照片时，通常会产生莫尔条纹，严重降低照片质量。在本文中，我们设计了一个基于小波的双分支网络（WDNet），具有用于图像去梦的空间注意机制。在 RGB 域中工作的现有图像恢复方法难以区分莫尔图案和真实场景纹理。与这些方法不同，我们的网络去除了小波域中的莫尔条纹，以将莫尔条纹的频率与图像内容分开。该网络结合了支持大感受野的密集卷积模块和扩张卷积模块。大量实验证明了我们方法的有效性，我们进一步表明 WDNet 可以推广到去除非屏幕图像上的摩尔纹伪影。虽然是为图像消解而设计的，