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纪勋, 冷娜, 郭慧. 水下图像增强与复原技术进展与展望[J]. 计算机辅助设计与图形学学报.
引用本文: 纪勋, 冷娜, 郭慧. 水下图像增强与复原技术进展与展望[J]. 计算机辅助设计与图形学学报.
Xun Ji, Na Leng, Hui Guo. Underwater Image Enhancement and Restoration Techniques: State-of-the-Art Methods and Prospects[J]. Journal of Computer-Aided Design & Computer Graphics.
Citation: Xun Ji, Na Leng, Hui Guo. Underwater Image Enhancement and Restoration Techniques: State-of-the-Art Methods and Prospects[J]. Journal of Computer-Aided Design & Computer Graphics.

水下图像增强与复原技术进展与展望

Underwater Image Enhancement and Restoration Techniques: State-of-the-Art Methods and Prospects

  • 摘要:   近年来, 水下图像增强与复原技术已成为促进水下目标检测、海洋生物识别、海底测绘等领域发展的重要手段, 具有重要的学术意义与应用价值. 首先, 回顾并分析了水下成像机理, 同时指出了当前水下成像技术由硬件设备、气候约束、人造光源以及粒子干扰这四方面所带来的难点与挑战; 其次, 全面介绍了水下图像增强与复原技术相关工作, 分析了两种技术的联系与区别, 同时从技术路线的角度对当前主流水下图像增强与复原方法进行了详细的分类与讨论, 并通过实验指明了不同分类方法的各自特点; 再次, 总结了常用的水下图像数据集与图像评价指标, 从数据支撑以及质量评估的方面为当前水下图像增强与复原工作提供了全面详实的技术指导; 最后, 预测并分析了未来水下图像增强与复原技术在应用场景、降质方式以及评价指标3个方面的可行发展方向, 尤其指明了当前人工智能在水下图像增强与复原领域中所能发挥的巨大潜力以及实用价值.

     

    Abstract:         In recent years, underwater image enhancement and restoration technology has become an important means to promote the development of underwater target detection, marine biological recognition, seabed surveying, and other fields, which has significant academic importance and application value. Firstly, the mechanism of underwater imaging is reviewed and analyzed, and the difficulties and challenges brought by hardware equipment, climate constraints, artificial light sources, and particle interference in current underwater imaging technology are pointed out. Secondly, the related works of underwater image enhancement and restoration technology are comprehensively introduced, and the relationship and difference between the two schemes are analyzed. Meanwhile, the current mainstream underwater image enhancement and restoration methods are classified and discussed in detail from the perspectives of technical routes, and the characteristics of different classification methods are pointed out through experiments. Thirdly, the commonly used underwater image datasets and image evaluation indicators are summarized, providing comprehensive and detailed technical guidance for current underwater image enhancement and restoration work from the perspectives of data support and quality evaluation. Finally, the feasible development directions of future underwater image enhancement and restoration technology in application scenarios, degradation methods, and evaluation indicators are predicted and analyzed, especially highlighting the enormous potential and practical value that current artificial intelligence can play in the field of underwater image enhancement and restoration.

     

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