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Towards Unified Deep Image Deraining: A Survey and a New Benchmark.

Xiang Chen, Jinshan Pan, Jiangxin Dong

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    This study introduces a unified evaluation framework for image deraining methods, addressing inconsistencies in current research. A new benchmark, HQ-RAIN, and an online toolkit are presented to standardize performance assessment and advance the field.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Significant advancements in image deraining have emerged from improved image priors and deep learning.
    • Existing deraining methods lack standardized evaluation settings, hindering fair comparison and assessment of practical utility.
    • Previous surveys on image deraining have not focused on unifying evaluation criteria.

    Purpose of the Study:

    • To provide a comprehensive review of current image deraining techniques.
    • To establish a unified evaluation setting for assessing deraining method performance.
    • To introduce a new benchmark dataset and an online platform for reproducible research.

    Main Methods:

    • A thorough review of existing image deraining approaches was conducted.
    • A unified evaluation framework was developed to standardize performance assessment.
    • A new high-quality benchmark dataset, HQ-RAIN, comprising 5,000 high-resolution synthetic images, was constructed.
    • An online toolkit was developed to facilitate the reproduction and tracking of deraining technologies.

    Main Results:

    • The study establishes a unified evaluation setting for image deraining methods.
    • The HQ-RAIN benchmark provides a high-quality, realistic dataset for extensive evaluations.
    • The developed online platform offers an off-the-shelf toolkit for large-scale performance evaluation.

    Conclusions:

    • A standardized approach to evaluating image deraining methods is crucial for fair comparison and practical application.
    • The HQ-RAIN benchmark and online toolkit facilitate reproducible research and track the latest advancements in image deraining.
    • Future research should address identified challenges and explore new opportunities in image deraining technology.