Progressive Hybrid-Modulated Network for Single Image Deraining

Xiaoyuan Yu, Guidong Zhang, Fei Tan, Fengguo Li and Wei Xie ()
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Xiaoyuan Yu: School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
Guidong Zhang: School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Fei Tan: School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
Fengguo Li: School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China
Wei Xie: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China

Mathematics, 2023, vol. 11, issue 3, 1-20

Abstract: Rainy degeneration damages an image’s visual effect and influences the performance of subsequent vision tasks. Various deep learning methods for single image deraining have been proposed, obtaining appropriate recovery results. Unfortunately, most existing methods ignore the interaction between rain-layer and rain-free components when extracting relevant features, leading to undesirable results. To break the above limitations, we propose a progressive hybrid-modulated network (PHMNet) for single image deraining based on the two-branch and coarse-to-fine framework. Specifically, a hybrid-modulated module (HMM) with a two-branch framework is proposed to blend and modulate the feature of rain-free layers and rain streaks. After cascading several HMMs in the coarsest reconstructed stage of the PHMNet, a multi-level refined module (MLRM) is adopted to refine the final deraining results in the refined reconstructed stage. By being trained using loss functions such as contrastive learning, the PHMNet can obtain satisfactory deraining results. Extended experiments on several datasets and downstream tasks demonstrate that our method performs favorably against state-of-the-art methods in quantitative evaluation and visual effects.

Keywords: image deraining; hybrid-modulation feature; two-branch framework (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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