FreqMamba: Viewing Mamba from a Frequency Perspective for Image
  Deraining
Zou Zhen Yu Hu Zhao Feng
Images corrupted by rain streaks often lose vital frequency information for
perception, and image deraining aims to solve this issue which relies on global
and local degradation modeling. Recent studies have witnessed the effectiveness
and efficiency of Mamba for perceiving global and local information based on
its exploiting local correlation among patches, however, rarely attempts have
been explored to extend it with frequency analysis for image deraining,
limiting its ability to perceive global degradation that is relevant to
frequency modeling (e.g. Fourier transform). In this paper, we propose
FreqMamba, an effective and efficient paradigm that leverages the complementary
between Mamba and frequency analysis for image deraining. The core of our
method lies in extending Mamba with frequency analysis from two perspectives:
extending it with frequency-band for exploiting frequency correlation, and
connecting it with Fourier transform for global degradation modeling.
Specifically, FreqMamba introduces complementary triple interaction structures
including spatial Mamba, frequency band Mamba, and Fourier global modeling.
Frequency band Mamba decomposes the image into sub-bands of different
frequencies to allow 2D scanning from the frequency dimension. Furthermore,
leveraging Mamba's unique data-dependent properties, we use rainy images at
different scales to provide degradation priors to the network, thereby
facilitating efficient training. Extensive experiments show that our method
outperforms state-of-the-art methods both visually and quantitatively.