Adverse weather image restoration strives to recover clear images from those affected by various weather types, such as rain, haze, and snow. Each weather type calls for a tailored degradation removal approach due to its unique impact on images. Conversely, content reconstruction can employ a uniform approach, as the underlying image content remains consistent. Although previous techniques can handle multiple weather types within a single network, they neglect the crucial distinction between these two processes, limiting the quality of restored images. This work introduces a novel adverse weather image restoration method, called DDCNet, which decouples the degradation removal and content reconstruction process at the feature level based on their channel statistics. Specifically, we exploit the unique advantages of the Fourier transform in both these two processes: (1) the degradation information is mainly located in the amplitude component of the Fourier domain, and (2) the Fourier domain contains global information. The former facilitates channel-dependent degradation removal operation, allowing the network to tailor responses to various adverse weather types; the latter, by integrating Fourier's global properties into channel-independent content features, enhances network capacity for consistent global content reconstruction. We further augment the degradation removal process with a degradation mapping loss function. Extensive experiments demonstrate our method achieves state-of-the-art performance in multiple adverse weather removal benchmarks.

翻译：恶劣天气图像恢复旨在从受雨、雾、雪等多种天气影响的图像中恢复清晰图像。每种天气类型因其对图像特有的影响，需要定制化的退化去除方法。相反，由于底层图像内容保持一致，内容重建可采用统一的方法。尽管现有技术能在单一网络中处理多种天气类型，但它们忽略了这两个过程之间的关键区别，从而限制了恢复图像的质量。本文提出了一种新颖的恶劣天气图像恢复方法DDCNet，该方法基于通道统计特性，在特征层面将退化去除与内容重建过程解耦。具体而言，我们利用了傅里叶变换在这两个过程中的独特优势：（1）退化信息主要位于傅里叶域的幅度分量中，（2）傅里叶域包含全局信息。前者有助于实现通道相关的退化去除操作，使网络能针对不同恶劣天气类型定制响应；后者通过将傅里叶全局特性整合到通道无关的内容特征中，增强了网络进行一致全局内容重建的能力。我们还通过退化映射损失函数进一步增强了退化去除过程。大量实验表明，我们的方法在多个恶劣天气去除基准测试中达到了最先进的性能。