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.

介绍了一种名为 DDCNet 的新型逆风气象图像恢复方法，通过基于信道统计的特征级别对降解去除过程和内容重建过程进行分离，利用傅里叶变换在这两个过程中的独特优势，进而提高恶劣天气图像恢复的质量。