@article{Zhuang2025, 
author = {Peixian Zhuang and Xinheng Zhang and Junjie Tong and Chenyu Wang and Jieyu Yuan and Chongyi Li},
title = {Underwater light field imaging: A survey},
year = {2025},
journal = {CAAI Artificial Intelligence Research},
volume = {4},
pages = {9150051},
keywords = {light field, influencing factors, underwater imaging, foundational theory, advanced applications},
url = {https://www.sciopen.com/article/10.26599/AIR.2025.9150051},
doi = {10.26599/AIR.2025.9150051},
abstract = {Underwater light field (ULF) imaging has emerged as a promising technology for capturing a more comprehensive array of visual information from real-world underwater environments. Unlike conventional underwater photography, which captures a two-dimensional projection of light that integrates over the angular domain, ULF imaging collects radiance from multiple directions. This enables the recovery of angular information that is typically lost in traditional imaging methods. Although ULF presents high-dimensional challenges such as inaccurate modeling and complex feature extraction, its ability to represent underwater visual data enhances the understanding of marine scenes. This, in turn, significantly improves the performance of various underwater vision tasks. The field of ULF imaging has garnered increasing attention in both the computer vision and computer graphics communities. This paper presents a comprehensive overview of the research conducted in this area over the past two decades. We focus on various aspects of ULF imaging, including ULF models, theory progression, parameter calibration, external and internal influencing factors, underwater scattering and refraction removal, underwater image enhancement/restoration, expansion of underwater imaging distance, underwater object detection, and underwater 3D reconstruction. Additionally, we analyze the current challenges facing ULF imaging technology and explore potential directions for its future development.}
}