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Low-Light Image and Video Enhancement Using Deep Learning: A Survey.

Chongyi Li, Chunle Guo, Linghao Han

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 9, 2021
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    Summary

    This survey provides a comprehensive overview of low-light image enhancement (LLIE) techniques, introducing a new dataset and online platform to advance research in the field.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Low-light image enhancement (LLIE) is crucial for improving image quality in poor illumination.
    • Deep learning methods dominate recent advances in LLIE, employing diverse strategies.

    Purpose of the Study:

    • To provide a comprehensive survey of LLIE methods, covering algorithms, datasets, and evaluation metrics.
    • To introduce a novel low-light image and video dataset captured with various mobile phones.
    • To establish a unified online platform for evaluating LLIE methods.

    Main Methods:

    • Conducted a comprehensive literature review and taxonomy of LLIE algorithms.
    • Created a new dataset with diverse low-light images and videos from mobile cameras.
    • Developed an online platform for user-friendly evaluation of LLIE methods.
    • Performed qualitative and quantitative evaluations on benchmark and proposed datasets.
    • Validated LLIE method performance on a face detection task in dark conditions.

    Main Results:

    • The survey categorizes existing LLIE approaches and identifies open research challenges.
    • The proposed dataset offers diverse real-world low-light scenarios for robust method evaluation.
    • The online platform provides accessible benchmarking and comparison of popular LLIE techniques.
    • Evaluations reveal varying performance of existing methods across different datasets and tasks.
    • Face detection performance is significantly impacted by the effectiveness of LLIE.

    Conclusions:

    • The survey, dataset, and platform offer a valuable resource for the LLIE research community.
    • The proposed resources aim to standardize evaluation and accelerate progress in low-light image enhancement.
    • Future research can leverage these resources to develop more robust and generalizable LLIE solutions.