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Multi-aggregation network based on non-separable lifting wavelet for single image deraining

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Abstract

Recently, many methods have utilized Haar wavelet to extract frequency-domain information for image deraining. However, Haar wavelet and other tensor product wavelets only capture high frequencies in horizontal, vertical, and diagonal directions, leading to the loss of high-frequency details in deraining methods. To address this issue, we propose a multi-aggregation network (MAGNet) based on non-separable lifting wavelet transform (NLWT), where NLWT is employed to capture high-frequency rain streaks in various directions. MAGNet aggregates neighboring features and incorporates outer skip connections based on the U-Net architecture, effectively utilizing the complementary information of rain patterns in different features. Additionally, a gated fusion module is used to fuse the aggregated features, capturing important rain pattern information and reducing feature redundancy. Moreover, MAGNet employs a scale-guide progressive fusion module to exploit the similarity between rain patterns at adjacent scales for deraining. Experiments on rainy datasets and a joint rain removal and object detection task demonstrate that our MAGNet outperforms advanced methods. The code is available at https://github.com/fashyon/MAGNet.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 61471160).

Funding

This work was supported by the National Natural Science Foundation of China (No. 61471160).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Hubei University, Wuhan, 430062, Hubei, China

    Bin Liu & Siyan Fang

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  1. Bin Liu
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  2. Siyan Fang
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Contributions

BL: Conceptualization, validation, writing—review & editing, Supervision, project administration, funding acquisition. SF: Methodology, software, formal analysis, investigation, data curation, writing—original draft, visualization.

Corresponding author

Correspondence to Siyan Fang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Communicated by J. Gao.

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Liu, B., Fang, S. Multi-aggregation network based on non-separable lifting wavelet for single image deraining. Multimedia Systems 29, 3669–3684 (2023). https://doi.org/10.1007/s00530-023-01156-0

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