Toward Efficient Underwater Visual Perception through Image Enhancement, Compression, and Understanding
Rongxin Zhu, Lei Sheng, Kaitao Wu, Azzedine Boukerche, Libo Long, Qiuling Yang · ACM Computing Surveys · 2025
The growing demand for marine exploration, environmental monitoring, and autonomous underwater operations has elevated the role of underwater image processing in both research and practical applications. However, the acquisition and transmission of underwater visual data are fundamentally constrained by the harsh aquatic environment, where factors such as limited bandwidth, strong light scattering, color distortion, and complex noise severely degrade image quality and restrict data throughput. These challenges not only hinder real-time perception and decision-making but also affect the efficiency of data-driven tasks such as mapping, object recognition, and navigation. To address these issues, a broad spectrum of underwater image processing methods has emerged, aiming to enhance visual clarity, compress data for efficient transmission, restore degraded signals, and enable accurate scene understanding. This survey provides a structured and comprehensive review of existing techniques, categorizing them into four core domains: image enhancement, image restoration, image compression and segmentation, and image classification. Representative methods within each domain are critically analyzed in terms of their underlying principles, computational complexity, and applicability across diverse underwater scenarios. Furthermore, the survey highlights emerging trends including deep learning-based approaches, cross-modal information fusion, and resource-efficient designs, offering insights for future development in underwater visual computing and communication systems.