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    罗钟铉

    • 教授     博士生导师   硕士生导师
    • 主要任职:党委常委、副校长
    • 性别:男
    • 毕业院校:大连理工大学
    • 学位:博士
    • 所在单位:软件学院、国际信息与软件学院
    • 学科:软件工程. 计算机应用技术
    • 办公地点:大连理工大学主楼
    • 联系方式:+86-411-84708315
    • 电子邮箱:zxluo@dlut.edu.cn

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    Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light

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    论文类型:期刊论文

    第一作者:刘日升

    通讯作者:樊鑫,朱明,Hou, Minjun,罗钟铉

    发表时间:2021-02-02

    发表刊物:IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

    卷号:30

    期号:12

    页面范围:4861-4875

    ISSN号:1051-8215

    关键字:Image color analysis; Image enhancement; Task analysis; Histograms; Benchmark testing; Imaging; Degradation; Underwater image enhancement; benchmark; visibility; color cast; object detection

    摘要:Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years. These algorithms developed upon various assumptions demonstrate successes from various aspects using different data sets and different metrics. In this work, we setup an undersea image capturing system, and construct a large-scale Real-world Underwater Image Enhancement (RUIE) data set divided into three subsets. The three subsets target at three challenging aspects for enhancement, i.e., image visibility quality, color casts, and higher-level detection/classification, respectively. We conduct extensive and systematic experiments on RUIE to evaluate the effectiveness and limitations of various algorithms to enhance visibility and correct color casts on images with hierarchical categories of degradation. Moreover, underwater image enhancement in practice usually serves as a preprocessing step for mid-level and high-level vision tasks. We thus exploit the object detection performance on enhanced images as a brand new task-specific evaluation criterion. The findings from these evaluations not only confirm what is commonly believed, but also suggest promising solutions and new directions for visibility enhancement, color correction, and object detection on real-world underwater images. The benchmark is available at: https://github.com/dlut-dimt/Realworld-Underwater-Image-Enhancement-RUIE-Benchmark.