A Review on Image Enhancement and Restoration Techniques for Underwater Optical Imaging Applications

被引:10
作者
Deluxni, N. [1 ]
Sudhakaran, Pradeep [1 ]
Kitmo [2 ]
Ndiaye, Mouhamadou Falilou [3 ]
机构
[1] SRM Inst Sci & Technol, Dept Comp Technol, Chengalpattu 603203, Tamil Nadu, India
[2] Univ Maroua, Natl Adv Sch Engn, Dept Renewable Energy, Maroua, Cameroon
[3] Univ Cheikh Anta Diop, Ecole Super Polytech, Lab Eau Energie Environm Procedes Ind, Dakar 5085, Senegal
关键词
Underwater image enhancement; restoration; dark channel prior; fusion based method; underwater network; Rayleigh distribution; adaptive histogram equalization; QUALITY ENHANCEMENT; BACKGROUND LIGHT; COLOR; VISIBILITY; CONTRAST; OPTIMIZATION; ALGORITHMS; FUSION; MODEL; WORLD;
D O I
10.1109/ACCESS.2023.3322153
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Underwater image processing always a challenging problem in oceanic engineering applications. Images captured in underwater are commonly suffers due to color distortion, detail blur, bluish or greenish tone, and low contrast to light scattering and absorption in the water medium. The image visibility is affected drastically during capturing caused by the degradation of light absorption and scattering effect. Hence, the effective Underwater Image Enhancement(UIE) and restoration techniques are primarily required for the underwater ecological study applications. Various UIE techniques are studied for different data sets, and applications. However, the selection of suitable method for particular applications among available techniques is still a challenging task. In this paper, an overview of recent UIE and restoration techniques and classification methods are elaborated with data sets and applications. The UIE techniques are grouped under various category such as spatial domain, transform domain, color constancy based method, retinex based approach. Similarly, the image restoration techniques are grouped under underwater optical imaging technique, polarization based approach, prior knowledge and convolutional neural networks. Finally, we review the research process of the underwater image enhancement and restoration with the essential background of the water images and recognize challenges.
引用
收藏
页码:111715 / 111737
页数:23
相关论文
共 99 条
[1]   Transform-based image enhancement algorithms with performance measure [J].
Agaian, SS ;
Panetta, K ;
Grigoryan, AM .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2001, 10 (03) :367-382
[2]   An Optical Image Transmission System for Deep Sea Creature Sampling Missions Using Autonomous Underwater Vehicle [J].
Ahn, Jonghyun ;
Yasukawa, Shinsuke ;
Sonoda, Takashi ;
Nishida, Yuya ;
Ishii, Kazuo ;
Ura, Tamaki .
IEEE JOURNAL OF OCEANIC ENGINEERING, 2020, 45 (02) :350-361
[3]   Color Balance and Fusion for Underwater Image Enhancement [J].
Ancuti, Codruta O. ;
Ancuti, Cosmin ;
De Vleeschouwer, Christophe ;
Bekaert, Philippe .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 27 (01) :379-393
[4]  
Ancuti C, 2012, PROC CVPR IEEE, P81, DOI 10.1109/CVPR.2012.6247661
[5]   Diving deeper into underwater image enhancement: A survey [J].
Anwar, Saeed ;
Li, Chongyi .
SIGNAL PROCESSING-IMAGE COMMUNICATION, 2020, 89
[6]  
Arredondo M, 2005, Oceans 2005 - Europe, Vols 1 and 2, P362
[7]  
Bai F., 2022, Underwater image restoration based on wavelet fusion and reweighted graph total variation prior, DOI [10.21203/rs.3.rs-1624223/v1, DOI 10.21203/RS.3.RS-1624223/V1]
[8]   Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset [J].
Berman, Dana ;
Levy, Deborah ;
Avidan, Shai ;
Treibitz, Tali .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 43 (08) :2822-2837
[9]   Color image simulation for underwater optics [J].
Boffety, Matthieu ;
Galland, Frederic ;
Allais, Anne-Gaelle .
APPLIED OPTICS, 2012, 51 (23) :5633-5642
[10]  
Brownlee J., 2019, Mach. Learn. Mastery