Scientific mapping and bibliometric analysis of research advancements in underwater image enhancement

• Published in: Journal of visual communication and image representation 2024-05, Vol.101, p.104166, Article 104166
• Main Authors: Huang, Yong, Chen, Renzhang
• Format: Article
• Language: English
• Subjects: Deep learning; Underwater image denoising; Underwater image enhancement; Underwater image fusion; Underwater image restoration
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2024.104166

Underwater Image Enhancement (UIE) addresses challenges in marine resource exploitation such as absorption, noise, and low contrast. This paper employs bibliometric methods and data from Web of Science to analyze UIE literature. The analysis reveals that research on UIE entered a rapid development stage starting from 2017. Three main areas of technical support for research: underwater image enhancement methods, deep learning-based methods, and underwater image restoration and depth estimation. Hotspots in research include enhancement, deep learning, restoration, feature extraction, and quality assessment. Drawing from the bibliometric analysis, this paper proposes a conceptual framework for UIE research and presents five research suggestions: enhancing method robustness and adaptability, improving real-time performance, integrating software and hardware, establishing underwater image benchmark datasets, and refining quality evaluation systems for underwater images. This study offers valuable references and guidance for the future exploration and development of the UIE field. •Bibliometric analysis of underwater image enhancement (UIE) literature using Web of Science data.•Three main technical areas: enhancement methods, deep learning, restoration/depth estimation.•Research hotspots: enhancement, deep learning, restoration, feature extraction, quality assessment.•Proposes conceptual framework and five suggestions for future UIE research direction.•Recommendations include enhancing robustness, real-time performance, software–hardware integration, benchmark datasets, refined quality evaluation.