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RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining.

Hong Wang, Qi Xie, Qian Zhao

    IEEE Transactions on Neural Networks and Learning Systems
    |April 5, 2023
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    Summary

    This study introduces a novel deep learning model, the Rain Convolutional Dictionary Network (RCDNet), for effective single image deraining. The RCDNet offers interpretable modules and dynamic kernel inference for superior performance across diverse rain conditions.

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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Image Processing

    Background:

    • Rain streaks degrade outdoor computer vision system performance.
    • Single image deraining is crucial for improving image quality.
    • Existing methods struggle with interpretability and generalization.

    Purpose of the Study:

    • Develop a novel, interpretable deep architecture for single image deraining.
    • Address the domain gap and generalization issues in real-world scenarios.
    • Improve the performance and robustness of image deraining techniques.

    Main Methods:

    • Proposed a Rain Convolutional Dictionary (RCD) model for rain streak representation.
    • Designed an iterative algorithm using proximal gradient descent.
    • Unfolded the algorithm into an interpretable deep network (RCDNet).
    • Introduced a dynamic RCDNet with inferred rain kernels for better generalization.

    Main Results:

    • RCDNet demonstrated superior deraining performance visually and quantitatively.
    • The dynamic RCDNet showed excellent generalization across diverse rain types.
    • All network modules exhibited clear physical meanings and interpretability.
    • Achieved state-of-the-art results compared to existing single image derainers.

    Conclusions:

    • The proposed RCDNet offers a highly interpretable and effective solution for single image deraining.
    • Dynamic kernel inference enhances generalization in varied and unseen rain conditions.
    • The method provides a robust framework for addressing image quality degradation due to rain.