Single image deraining refers to the process of restoring the clean background scene from a rainy image. Current approaches have resorted to deep learning techniques to remove rain from a single image by leveraging some prior information. However, due to the various appearances of rain streaks and accumulation, it is difficult to separate rain and background information in the embedding space, which results in inaccurate deraining. To address this issue, this paper proposes a selective wavelet attention learning method by learning a series of wavelet attention maps to guide the separation of rain and background information in both spatial and frequency domains. The key aspect of our method is utilizing wavelet transform to learn the content and structure of rainy features because the high-frequency features are more sensitive to rain degradations, whereas the low-frequency features preserve more of the background content. To begin with, we develop a selective wavelet attention encoder–decoder network to learn wavelet attention maps guiding the separation of rainy and background features at multiple scales. Meanwhile, we introduce wavelet pooling and unpooling to the encoder–decoder network, which shows superiority in learning increasingly abstract representations while preserving the background details. In addition, we propose latent alignment learning to supervise the background features as well as augment the training data to further improve the accuracy of deraining. Finally, we employ a hierarchical discriminator network based on selective wavelet attention to adversarially improve the visual fidelity of the generated results both globally and locally. Extensive experiments on synthetic and real datasets demonstrate that the proposed approach achieves more appealing results both quantitatively and qualitatively than the recent state-of-the-art methods.

单张图像清除是指从多雨图像中还原干净背景场景的过程。当前的方法已经诉诸于深度学习技术，以通过利用一些先验信息来去除单个图像中的雨水。然而，由于雨水条纹和积水的各种出现，难以在嵌入空间中分离雨水和背景信息，从而导致排水不准确。为了解决这个问题，本文提出了一种选择性的小波注意学习方法，该方法通过学习一系列小波注意图来指导在空间和频率域中降雨和背景信息的分离。我们方法的主要方面是利用小波变换来了解雨天要素的内容和结构，因为高频特征对雨天退化更敏感，而低频特征则保留了更多的背景内容。首先，我们开发一个选择性的小波注意编码器-解码器网络，以学习指导多尺度下雨天和背景特征分离的小波注意图。同时，我们在编码器-解码器网络中引入了小波合并和分拆，这在学习越来越抽象的表示形式同时保留背景细节方面显示出优势。此外，我们提出了潜在的对齐学习，以监督背景特征，并增加训练数据，以进一步提高排水的准确性。最后，我们采用基于选择性小波注意的分层鉴别器网络，以对抗性地提高全局和局部生成结果的视觉保真度。