Decoupling Degradation and Content Processing for Adverse Weather Image
  Restoration
Xi Wang Xueyang Fu Peng-Tao Jiang Jie Huang Mi Zhou Bo Li Zheng-Jun Zha
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.