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Attentive Feature Refinement Network for Single Rainy Image Restoration.

Guoqing Wang, Changming Sun, Arcot Sowmya

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 17, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Researchers developed a novel attentive feature refinement (AFR) module to address over/under-deraining issues in single image deraining tasks. This new method refines unsatisfactory features, achieving state-of-the-art results on synthetic and real images with improved efficiency.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Single image deraining remains challenging, often requiring multiple refinement stages.
    • Existing models suffer from over/under-deraining due to stage-independent learning.

    Purpose of the Study:

    • To propose a novel attentive feature refinement (AFR) module for improved single image deraining.
    • To resolve issues of over/under-deraining caused by current refinement methods.

    Main Methods:

    • Developed a two-branched AFR module for rain-distribution-aware attention map learning and refinement.
    • Integrated AFR modules into an encoder-decoder network (AFR-Net) for image deraining.
    • Employed knowledge distillation to train an efficient student model.

    Main Results:

    • AFR-Net achieved new state-of-the-art results on both synthetic and real-world deraining datasets.
    • The distilled student model offers state-of-the-art performance with significantly faster inference speeds.
    • The AFR module introduces minimal computational overhead and is easily integrated.

    Conclusions:

    • The proposed AFR module effectively refines unsatisfactory features for superior image deraining.
    • AFR-Net demonstrates a promising approach for high-performance and efficient single image deraining.
    • Knowledge distillation enables the creation of practical, fast, yet accurate deraining models.