From shallow sea to deep sea: research progress in underwater image restoration

doi:10.3389/fmars.2023.1163831

CORC > 深海科学与工程研究所 > 中国科学院深海科学与工程研究所 > 深海工程技术部 > 深海视频技术研究室

	From shallow sea to deep sea: research progress in underwater image restoration
	Song, Wei 4; Liu, Yaling 4; Huang, Dongmei 3; Zhang, Bing 2; Shen, Zhihao 4; Xu, Huifang 1
刊名	FRONTIERS IN MARINE SCIENCE
	2023-05-31
卷号	10 页码:24
关键词	shallow-sea image restoration deep-sea image restoration image formation physical model prior deep learning
DOI	10.3389/fmars.2023.1163831
通讯作者	Xu, Huifang(17069@gench.edu.cn)
英文摘要	Underwater images play a crucial role in various fields, including oceanographic engineering, marine exploitation, and marine environmental protection. However, the quality of underwater images is often severely degraded due to the complexities of the underwater environment and equipment limitations. This degradation hinders advancements in relevant research. Consequently, underwater image restoration has gained significant attention as a research area. With the growing interest in deep-sea exploration, deep-sea image restoration has emerged as a new focus, presenting unique challenges. This paper aims to conduct a systematic review of underwater image restoration technology, bridging the gap between shallow-sea and deep-sea image restoration fields through experimental analysis. This paper first categorizes shallow-sea image restoration methods into three types: physical model-based methods, prior-based methods, and deep learning-based methods that integrate physical models. The core concepts and characteristics of representative methods are analyzed. The research status and primary challenges in deep-sea image restoration are then summarized, including color cast and blur caused by underwater environmental characteristics, as well as insufficient and uneven lighting caused by artificial light sources. Potential solutions are explored, such as applying general shallow-sea restoration methods to address color cast and blur, and leveraging techniques from related fields like exposure image correction and low-light image enhancement to tackle lighting issues. Comprehensive experiments are conducted to examine the feasibility of shallow-sea image restoration methods and related image enhancement techniques for deep-sea image restoration. The experimental results provide valuable insights into existing methods for addressing the challenges of deep-sea image restoration. An in-depth discussion is presented, suggesting several future development directions in deep-sea image restoration. Three main points emerged from the research findings: i) Existing shallow-sea image restoration methods are insufficient to address the degradation issues in deep-sea environments, such as low-light and uneven illumination. ii) Combining imaging physical models with deep learning to restore deep-sea image quality may potentially yield desirable results. iii) The application potential of unsupervised and zero-shot learning methods in deep-sea image restoration warrants further investigation, given their ability to work with limited training data.
资助项目	National Natural Science Foundation of China[61972240] ; program for the capacity development of Shanghai local universities by Shanghai Science and Technology Commission[20050501900]
WOS关键词	ILLUMINATION ESTIMATION ; ENHANCEMENT ; COLOR ; NETWORK ; SCATTERING ; WEATHER
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:001005707200001
资助机构	National Natural Science Foundation of China ; program for the capacity development of Shanghai local universities by Shanghai Science and Technology Commission
内容类型	期刊论文
源URL	[http://ir.idsse.ac.cn/handle/183446/10352]
专题	深海工程技术部_深海视频技术研究室
通讯作者	Xu, Huifang
作者单位	1.Shanghai Jian Qiao Univ, Coll Informat Technol, Shanghai, Peoples R China 2.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya, Hainan, Peoples R China 3.Shanghai Univ Elect Power, Coll Elect & Informat Engn, Shanghai, Peoples R China 4.Shanghai Ocean Univ, Digital Ocean Lab, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Song, Wei,Liu, Yaling,Huang, Dongmei,et al. From shallow sea to deep sea: research progress in underwater image restoration[J]. FRONTIERS IN MARINE SCIENCE,2023,10:24.
APA	Song, Wei,Liu, Yaling,Huang, Dongmei,Zhang, Bing,Shen, Zhihao,&Xu, Huifang.(2023).From shallow sea to deep sea: research progress in underwater image restoration.FRONTIERS IN MARINE SCIENCE,10,24.
MLA	Song, Wei,et al."From shallow sea to deep sea: research progress in underwater image restoration".FRONTIERS IN MARINE SCIENCE 10(2023):24.