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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Continual Image Deraining With Hypergraph Convolutional Networks.

Xueyang Fu, Jie Xiao, Yurui Zhu

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    |April 6, 2023
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    This study introduces a novel image deraining framework using hypergraph convolutions and continual learning. The new method enhances performance on multiple datasets, overcoming limitations of existing deep learning approaches.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Image deraining is complex due to rain streak characteristics.
    • Current deep learning methods struggle with adaptability and catastrophic forgetting across datasets.

    Purpose of the Study:

    • To develop a novel image deraining framework.
    • To explore nonlocal similarity and enable continuous learning on multiple datasets.

    Main Methods:

    • Designed a patchwise hypergraph convolutional module for nonlocal property extraction.
    • Implemented a brain-inspired continual learning algorithm to balance stability and plasticity.
    • Addressed catastrophic forgetting for enhanced generalizability.

    Main Results:

    • Achieved state-of-the-art performance on synthetic datasets.
    • Demonstrated significantly improved generalizability on unseen real-world rainy images.
    • Unified parameters enabled handling of multiple datasets effectively.

    Conclusions:

    • The proposed framework offers superior deraining performance and adaptability.
    • Continual learning approach effectively mitigates catastrophic forgetting.
    • The method provides a robust solution for real-world image deraining challenges.