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Related Experiment Video

Updated: Nov 23, 2025

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Single-Image Deraining via Recurrent Residual Multiscale Networks.

Yupei Zheng, Xin Yu, Miaomiao Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |December 30, 2020
    PubMed
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    This study introduces a novel residual multiscale pyramid method for single-image deraining. The approach effectively removes complex rain streaks by progressively refining image details across different resolutions, achieving superior performance.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Existing single-image deraining methods struggle with the complexity of real-world rain due to difficulties in decomposing rain layers.
    • Rain streaks exhibit variations in density, shape, and direction, posing significant challenges for traditional layer separation techniques.

    Purpose of the Study:

    • To develop a novel and effective single-image deraining method.
    • To address the limitations of existing approaches in handling complex rain patterns.
    • To create a powerful yet compact deraining framework.

    Main Methods:

    • A residual multiscale pyramid approach is proposed for progressive rain streak removal in a coarse-to-fine manner.
    • Residuals between restored and rain images are utilized as attention maps for enhanced rain removal in finer resolution levels.

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  • A recurrent network architecture and a multiscale kernel selection network (MSKSN) are employed for efficient and adaptable deraining.

    • Main Results:

    • The proposed method successfully removes heavy rain at coarse levels and light rain at finer levels.
    • The use of residuals as attention maps significantly aids in identifying and eliminating rain streaks.
    • The network achieves a parameter reduction of 81% compared to prior work without performance degradation.

    Conclusions:

    • The novel residual multiscale pyramid method offers a superior solution for single-image deraining.
    • The attention-based residual approach and compact network design contribute to enhanced performance and efficiency.
    • The method demonstrates state-of-the-art deraining capabilities on widely used benchmarks.