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Multi-Scale Fusion and Decomposition Network for Single Image Deraining.

Qiong Wang, Kui Jiang, Zheng Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 7, 2023
    PubMed
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    This study introduces the Multi-scale Fusion and Decomposition Network (MFDNet) for effective image deraining. The novel network combines Convolutional Neural Networks (CNNs) and Self-Attention (SA) to enhance image restoration tasks.

    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Processing

    Background:

    • Convolutional Neural Networks (CNNs) excel at local feature extraction but have limited receptive fields.
    • Self-Attention (SA) mechanisms capture long-range dependencies but can be computationally inefficient and struggle with local details.
    • Existing methods often face limitations in balancing effectiveness and efficiency for low-level vision tasks.

    Purpose of the Study:

    • To propose a novel network, MFDNet, that unifies the strengths of CNNs and SA for superior image deraining.
    • To address the individual limitations of CNNs and SA in low-level vision tasks.
    • To develop an efficient and effective solution for rain removal and other image restoration challenges.

    Main Methods:

    • Developed a Multi-scale Fusion and Decomposition Network (MFDNet) integrating CNNs and SA.

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  • Introduced an asymmetrical dual-path mutual representation network for iterative refinement of rain and rain-free features.
  • Incorporated high-efficiency convolutions and resolution rescaling to optimize performance and computational complexity.

    • Main Results:

    • MFDNet demonstrates superior performance compared to state-of-the-art (SOTA) deraining methods.
    • The proposed approach shows versatility and robustness across various image restoration tasks.
    • Achieved a balance between effectiveness and computational efficiency in deraining.

    Conclusions:

    • MFDNet effectively unifies the benefits of CNNs and SA for image deraining.
    • The network architecture offers a robust and versatile solution for multiple image restoration problems.
    • The proposed method advances the state-of-the-art in low-level vision tasks.