数字海洋与水下攻防2025，Vol.8Issue(4)：382-389,8.DOI:10.19838/j.issn.2096-5753.2025.04.001
水下图像增强技术:挑战、进展与未来方向
Underwater Image Enhancement Technology:Challenges,Progress and Future Directions
摘要
Abstract
Underwater images represent a crucial form of visual information,serving fields such as marine scientific research and resource exploration.To obtain clear and high-quality images from degraded low-quality underwater images,effective image enhancement techniques are essential.In this paper,the degradation mechanisms and imaging models of underwater images are expounded based on the unique characteristics of underwater environment.Two mainstream techniques,namely traditional image processing-based underwater image enhancement and deep learning-driven underwater image enhancement,are reviewed.The limitations of physical model-based methods,non-physical model-based methods,convolutional neural network-based methods,and generative network-based approaches are analyzed.Then,the core challenges and corresponding solutions in current underwater image enhancement technologies are summarized.Furthermore,future research directions are prospected,including:deeper integration of physical models with deep learning;unsupervised/weakly-supervised/self-supervised learning;underwater image generation and high-quality dataset construction;multi-modal information fusion;lightweight design,high efficiency and embedded deployment;and objective evaluation metrics aligned with subjective perceptual consistency.关键词
水下图像增强/深度学习/图像生成/水下视觉Key words
underwater image enhancement/deep learning/image generation/underwater vision分类
信息技术与安全科学引用本文复制引用
袁容,吴帆,欧阳..水下图像增强技术:挑战、进展与未来方向[J].数字海洋与水下攻防,2025,8(4):382-389,8.基金项目
国家自然科学基金项目"多重损伤耦合作用的风电机组传动系统故障物理可靠性分析与设计优化"(52175130) （52175130）
四川省科技厅项目"基于时变载荷与多场耦合作用的风电机组传动系统动态可靠性分析"(2024NSFJQ0033). （2024NSFJQ0033）
