  Removing rain artifacts in images is recognized as a significant issue. In
this field, deep learning-based approaches, such as convolutional neural
networks (CNNs) and Transformers, have succeeded. Recently, State Space Models
(SSMs) have exhibited superior performance across various tasks in both natural
language processing and image processing due to their ability to model
long-range dependencies. This study introduces SSM to rain removal and proposes
a Deraining Frequency-Enhanced State Space Model (DFSSM). To effectively remove
rain streaks, which produce high-intensity frequency components in specific
directions, we employ frequency domain processing concurrently with SSM.
Additionally, we develop a novel mixed-scale gated-convolutional block, which
uses convolutions with multiple kernel sizes to capture various scale
degradations effectively and integrates a gating mechanism to manage the flow
of information. Finally, experiments on synthetic and real-world rainy image
datasets show that our method surpasses state-of-the-art methods.
