FPGNet: Single Image Deraining with High-Frequency Channel and Frequency Domain Prior Guidance

Abstract

In recent years, deep learning methods have shown promising results in Single Image Deraining (SID). However, these methods still suffer from unsatisfactory residual rain streaks, primarily due to the absence of image priors embedding and limitations in modeling capacity. In this paper, we propose a joint High-Frequency Channel and Fourier Frequency Domain Guided Network (FPGNet) to remove complex rain streaks while obtaining clearer background details. Specifically, FPGNet comprises two core components: the High-frequency Guidance Module (HFGM) and the Fourier-based Multi-scale Feature Extraction Module (FMFEM). The HFGM focuses on learning the natural distribution of rain streaks from the high-frequency channel to guide the network’s modeling of rain streaks, while the FMFEM serves as the backbone of FPGNet to learn rain layers in rainy images. Additionally, we introduce an Interactive Fusion Module (IFM) to enhance the integration of the high-frequency channel into the network. Extensive experiments demonstrate that our network outperforms state-of-the-art deraining methods in effectively removing rain streaks from rainy images.