Abstract
Cameras are integrated with various underwater vision systems for underwater object detection and marine biological monitoring. However, underwater images captured by cameras rarely achieve the desired visual quality, which may affect their further applications. Various underwater vision enhancement technologies have been proposed to improve the visual quality of underwater images in the past few decades, which is the focus of this paper. Specifically, we review the theory of underwater image degradations and the underwater image formation models. Meanwhile, this review summarizes various underwater vision enhancement technologies and reports the existing underwater image datasets. Further, we conduct extensive and systematic experiments to explore the limitations and superiority of various underwater vision enhancement methods. Finally, the recent trends and challenges of underwater vision enhancement are discussed. We wish this paper could serve as a reference source for future study and promote the development of this research field.
Similar content being viewed by others
References
Ren R, Zhang L, Liu L, Yuan Y (2021) Two AUVs guidance method for self-reconfiguration Mission based on monocular vision. IEEE Sensors J 21(8):10082–10090
Kim B, Kim J, Cho H, Kim J, Yu S (2020) AUV-based multi-view scanning method for 3-D reconstruction of underwater object using forward scan sonar. IEEE Sens J 20(3):1592–1606
Li C, Guo J, Guo C (2018) Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal Process Lett 25(3):323–327
Zhou J, Zhang D, Zhang W (2020) The classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey. Front Inform Technol Elect Eng 21(12):1745–1769
Han M, Lyu Z, Qiu T, Xu M (2020) A review on intelligence Dehazing and color restoration for underwater images. IEEE Trans Syst Man Cybern Syst 50(5):1820–1832
Lu H, Li Y, Zhang Y, Chen M, Serikawa S, Kim H (2017) Underwater optical vision enhancement: a comprehensive review. Mobile Netw Appl 22(6):1204–1211
Wang Y, Song W, Fortino G, Qi L-Z, Zhang W, Liotta A (2019) An experimental-based review of image enhancement and image restoration methods for underwater imaging. IEEE Access 7:140233–140251
Schechner YY, Karpel N (2005) Recovery of underwater visibility and structure by polarization analysis. IEEE J Ocean Eng 30(3):570–587
Treibitz T, Schechner YY (2006) Instant 3Descatter. In: Proc. CVPR, New York, USA, pp 1861–1868
Treibitz T, Schechner YY (2009) Active polarization descattering. IEEE Trans Pattern Anal Mach Intell 31(3):385–399
Hu H, Zhao L, Huang B, Li X, Wang H, Liu T (2017) Enhancing visibility of polarimetric underwater image by transmittance correction. IEEE Photonics J 9(3):1–10
Hu H, Zhao L, Li X, Wang H, Yang J, Li K, Liu T (2018) Polarimetric image recovery in turbid media employing circularly polarized light. Opt Express 26(19):25047–25059
Huang BJ, Liu T, Hu H, Han JH, Yu MX (2016) Underwater image recovery considering polarization effects of objects. Opt Express 24(9):9826–9988
Wu HD, Zhao M, Xu WH (2020) Underwater de-scattering imaging by laser field synchronous scanning. Opt Lasers Eng 126:1–8
Ishibashi S (2011) The study of the underwater camera model. In: Proc. OCEANS, Santander, Spain, pp 1–6
Pizerr SM, Amburn EP, Austin JD et al (1987) Adaptive histogram equalization and its variations. Comput Gr Image Process 39(3):355–368
Bruno F, Bianco G, Muzzupappa M, Barone S, Razionale AV (2011) Experimentation of structured light and stereo vision for underwater 3D reconstruction. ISPRS-J Photogramm Remote Sens 66(4):508–510
Roser M, Dunbabin M, Geiger A (2014) Simultaneous underwater visibility assessment enhancement and improved stereo. In: Proc. ICPA, Hong Kong, China, pp 3840–3847
Lin Y, Chen S, Tsou C (2019) Development of an vision enhancement module for autonomous underwater vehicles through integration of visual recognition with stereoscopic image reconstruction. J Mar Sci Eng 7(4):1–42
Luczynski T, Luczynski P, Pehle L, Wirsum M, Birk A (2019) Model based design of a stereo vision system for intelligent deep-sea operations. Measurement 144:298–310
Tan CS, Sluzek A, Seet G, Jiang TY (2006) Range gated imaging system for underwater robotic vehicle. In: Proc. OCEANS, Singapore, pp 1–6
Li H, Wang X, Bai T, Jin W, Ding K (2009) Speckle noise suppression of range gated underwater imaging system. In: Proc. SPIE, California, USA, pp 1–8
Liu W, Li Q, Hao G, Wu G, Lv P (2018) Experimental study on underwater range-gated imaging system pulse and gate control coordination strategy. In: Proc. SPIE, Beijing, China
Wang M, Wang X, Sun L, Yang Y, Zhou Y (2020) Underwater 3D deblurring-gated range-intensity correlation imaging. Opt Lett 45(6):1455–1458
Wang M, Wang X, Zhang Y, Sun L, Lei P, Yang Y, Chen J, He J, Zhou Y (2021) Range-intensity-profile prior dehazing method for underwater range-gated imaging. Opt Express 29(5):7630–7640
Han P, Liu F, Wei Y, Shao X (2020) Optical correlation assists to enhance underwater polarization imaging performance. Opt Lasers Eng 134:1–6
Liu TG, Guan ZJ, Li XB, Chen ZZ, Han YD, Yang JY, Li K, Zhao JY, Hu HF (2020) Polarimetric underwater image recovery for color image with crosstalk compensation. Opt Lasers Eng 124:1–6
Tyo JS, Rowe MP, Pugh EN, Engheta N (1996) Target detection in optically scattering media by polarization-difference imaging. Appl Opt 35(11):1855–1870
McGlamery BL (1980) A computer model for underwater camera systems. In: Proc. SPIE, Monterey, USA, pp 221–231
Jaffe JS (1990) Computer modeling and the design of optimal underwater imaging systems. IEEE J Ocean Eng 15(2):101–111
Huo F, Zhu X, Zeng H, Liu Q, Qiu J (2021) Fast fusion-based dehazing with histogram modification and improved atmospheric illumination prior. IEEE Sensors J 21(4):5259–5270
Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):1–9
Tan RT (2008) Visibility in bad weather from a single image. In: Proc. CVPR, Anchorage, AK, USA, pp 1–8
He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353
Chiang JY, Chen Y (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756–1769
Wen H, Tian Y, Huan T, Gao W (2013) Single underwater image enhancement with a new optical model. In: Proc. ISCAS, Beijing, China, pp 753–756
Drews P, Nascimento ER, Moraes F, Botelho S, Campos M (2013) Transmission estimation in underwater single images. In: Proc. ICCV, Sydney, NSW, pp 825–830
Galdran A, Pardo D, Picòn A, Álvarez-Gila A (2015) Automatic red-channel underwater image restoration. J Vis Commun Image Represent 26:132–145
Peng Y, Cao K, Cosman PC (2018) Generalization of the dark channel prior for single image restoration. IEEE Trans Image Process 27(6):2856–2868
Zhou JC, Liu ZZ, Zhang WD, Zhang DH, Zhang WS (2020) Underwater image restoration based on secondary guided transmission map. Multimed Tools Appl 80:7771–7788
Lee HS, Moon SW, Eom IK (2020) Underwater image enhancement using successive color correction and superpixel dark channel prior. Symmetry 12(8):1–18
Carlevaris-Bianco N, Mohan A, Eustice RM (2010) Initial results in underwater single image dehazing. In: Proc. OCEANS, Seattle, USA, pp 1–8
Peng Y, Zhao X, Cosman P (2015) Single underwater image enhancement using depth estimation based on blurriness. In: Proc. ICIP, Quebec City, pp 4952–4956
Peng Y, Cosman P (2017) Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 26(4):1579–1594
Emberton S, Chittka L, Cavallaro A (2015) Hierarchical rank-based veiling light estimation for underwater dehazing. In: Proc. BMVC, Swansea, UK, pp 1–12
Berman D, Levy D, Avidan S, Treibitz T (2020) Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE Trans Pattern Anal Mach Intell:1–13
Peng Y, Cosman P (2016) Single image restoration using scene ambient light differential. In: Proc. ICIP, Phoenix, pp 1953–1957
Ancuti CO, Ancuti C, Vleeschouwer CD, Garcia R, Bovik AC (2016) Multi-scale underwater descattering. In: Proc. ICPR, Cancun, pp 4202–4207
Li C, Guo J, Cong R, Pang Y, Wang B (2016) Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 25(12):5664–5677
Ancuti CO, Ancuti C, Vleeschouwer CD, Neumann L, Garcia R (2017) Color transfer for underwater dehazing and depth estimation. In: Proc. ICIP, Beijing, China, pp 695–699
Dai CG, Lin MX, Wu XJ, Wang Z, Guan ZG (2019) Single underwater image restoration by decomposing curves of attenuating color. Opt Laser Technol 123(5):1–11
Cho Y, Shin Y, Kim A (2016) Online depth estimation and application to underwater image dehazing. In: Proc. OCEANS. Monterey, CA, pp 1–7
Yang M, Sowmya A, Wei Z, Zheng B (2020) Offshore underwater image restoration using reflection-decomposition-based transmission map estimation. IEEE J Ocean Eng 45(2):521–533
Drews P, Nascimento ER, Campos MFM, Elfes A (2015) Automatic restoration of underwater monocular sequences of images. In: Proc. IROS, Hamburg, pp 1058–1064
Li Z, Tan P, Tan RT, Zou D, Zhou ZS, Cheong L (2015) Simultaneous video defogging and stereo reconstruction. In: Proc. CVPR, Boston, MA, pp 4988–4997
Emberton S, Chittka L, Cavallaro A (2018) Underwater image and video dehazing with pure haze region segmentation. Comput Vis Image Underst 168:145–156
Xu L, Ren JS, Liu C, Jia J (2014) Deep convolutional neural network for image deconvolution. In: Proc. NIPS, MA, USA, pp 1790–1798
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. CVPR, Las Vegas, pp 770–778
Cai B, Xu X, Jia K, Qing C, Tao D (2016) DehazeNet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198
Fu XY, Cao XY (2020) Underwater image enhancement with global-local networks and compressed-histogram equalization. Signal Process-Image Commun 86:1–15
Raihan J, Abas PE, Silva LCD (2019) Review of underwater image restoration algorithms. IET Signal Process 13(10):1587–1596
Shin Y, Cho Y, Pandey G, Kim A (2016) Estimation of ambient light and transmission map with common convolutional architecture. In: Proc. OCEANS, Monterey, Ca, pp 1–7
Wang Y, Zhang J, Cao Y, Wang Z (2017) A deep CNN method for underwater image enhancement. In: Proc. ICIP, Beijing, China, pp 1382–1386
Zhang S, Zhang J, Fang S, Cao Y (2014) Underwater stereo image enhancement using a new physical model. In: Proc. ICIP, Paris, France, pp 5422–5426
Li C, Anwar S, Porikli F (2020) Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn 98(1):1–11
Lu H, Li Y, Uemura T, Kim H, Serikawa S (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur. Gener. Comp. Syst 82:142–148
Wang K, Hu Y, Chen J, Wu X, Zhao X, Li Y (2019) Underwater image restoration based on a parallel convolutional neural network. Remote Sens 11(13):1–21
Ren W, Ma L, Zhang J, Pan J, Cao X, Liu W, Yang M (2018) Gated fusion network for single image dehazing. In: Proc. CVPR, Salt Lake City, UT, pp 3253–3261
Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2019) An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process 29:4376–4389
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proc. NIPS, Montreal, Canada, pp 2672–2680
Chen X, Yu J, Kong S, Wu Z, Fang X, Wen L (2019) Towards real-time advancement of underwater visual quality with GAN. IEEE Trans Ind Electron 66(12):9350–9359
Fabbri C, Islam MJ, Sattar J (2018) Enhancing underwater imagery using generative adversarial networks. In: Proc. ICRA, Brisbane, QLD, Australia, pp 7159–7165
Li J, Skinner KA, Eustice RM, Johnson-Roberson M (2018) WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot Autom Lett 3(1):387–394
Zhu J, Park T, Isola P, Efros AA (2017) Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. In: Proc. ICCV, Venice, pp 2242–2251
Pizerr SM, Amburn EP, Austin JD et al (1987) Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Vision Enhancement 39(3):355–368
Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Proc. Graphics Gems IV. Academic Press Professional, pp 474–485
Hitam MS, Awalludin EA, Jawahir WN, Yussof WNJHW, Bachok Z (2013) Mixture contrast limited adaptive histogram equalization for underwater image enhancement. In: Proc. ICCAT, Sousse, Tunisia, pp 1–5
Luo W, Duan S, Zheng J (2021) Underwater image restoration and enhancement based on a fusion algorithm with color balance, contrast optimization, and histogram stretching. IEEE Access 9:31792–31804
Ancuti C, Ancuti CO, Haber T, Bekaert P (2012) Enhancing underwater images and videos by fusion. In: Proc. CVPR, Providence, USA, pp 81–88
Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2018) Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 27(1):379–393
Gao FR, Wang K, Zhang ZY, Wang YJ, Zhang QZ (2021) Underwater image enhancement based on local contrast correction and multi-scale fusion. J Mar Sci Eng 9(2):1–16
Song HJ, Wang R (2021) Underwater image enhancement based on multi-scale fusion and global stretching of dual-model. Mathematics 9(6):1–14
Jobson DJ, Rahman Z, Woodell GA (1997) Properties and performance of a center/surround retinex. IEEE Trans Image Process 6(3):451–462
Jobson DJ, Rahman Z, Woodell GA (1997) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process 6(7):965–976
Zhang S, Wang T, Dong JY, Yu H (2017) Underwater image enhancement via extended multi-scale Retinex. Neurocomputing 245:1–9
Tang C, von Lukas UF, Vahl M, Wang S, Tan M (2019) Efficient underwater image and video enhancement based on Retinex. Signal Image Video Process 13:1011–1018
Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database. In: Proc. CVPR, Miami, FL, USA, pp 248–255
Liu R, Fan X, Zhu M, Hou M, Luo Z (2020) Real-world underwater enhancement: challenges, benchmarks, and solutions under natural light. IEEE Trans Circ Syst Video Technol 30(12):4861–4875
Akkaynak D, Treibitz T (2019) Sea-Thru: a method for removing water from underwater images. In: Proc. CVPR, pp 1682–1691
Islam MJ, Xia Y, Sattar J (2020) Fast underwater image enhancement for improved visual perception. IEEE Robot Autom Lett 5(2):3227–3234
Song W, Wang Y, Huang D, Liotta A, Perra C (2020) Enhancement of underwater images with statistical model of background light and optimization of transmission map. IEEE Trans Broadcast 66(1):153–169
Fu X, Fan Z, Ling M, Huang Y, Ding X (2017) Two-step approach for single underwater image enhancement. In: Proc. ISPAC, pp 789–794
Fu X, Zhuang P, Huang Y, Liao Y, Zhang X, Ding X (2014) A retinex-based enhancing approach for single underwater image. In: Proc. ICIP, pp 4572–4576
Panetta K, Gao C, Agaian S (2016) Human-visual-system-inspired underwater image quality measures. IEEE J Ocean Eng 41(3):541–551
Yang M, Sowmya A (2015) An underwater color image quality evaluation metric. IEEE Trans Image Process 24(12):6062–6071
Wang S, Ma K, Yeganeh H, Wang Z, Lin W (2015) A patch-structure representation method for quality assessment of contrast changed images. IEEE Signal Process Lett 22(12):2387–2390
Wang Y, Li N, Li Z, Gu Z, Zheng H, Zheng B, Sun M (2018) An imaging-inspired no-reference under water color image quality assessment metric. Comput Electr Eng 70:904–913
Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Akkaynak D, Treibitz T, Shlesinger T, Tamir R, Loya Y, Iluz D (2017) What is the space of attenuation coefficients in underwater computer vision? In: Proc. IEEE CVPR, pp 568–577
Akkaynak D, Treibitz T (2018) A revised underwater image formation model. In: Proc. CVPR, pp 6723–6732
Yang L, Chao X, Ercisli S (2022) Disturbed-entropy: a simple data quality assessment approach. ICT Express:1–4
Yang L, Yang J, Wen J (2021) Entropy-based redundancy analysis and information screening. Digit Commun Netw:1–11
Yang L, Chao X (2022) Distance-entropy: an effective Indicator for selecting informative data. Front Plant Sci 12:1–8
Zhou J, Yang T, Chu W, Zhang W (2022) Underwater image restoration via backscatter pixel prior and color compensation. Eng. Appl. Artif. Intell 111: 104785:1–16
Acknowledgments
The authors would like to thank the anonymous reviewers for their constructive and valuable comments.
This work was supported in part by the National Natural Science Foundation of China under Grant 61702074, in part by the Liaoning Provincial Natural Science Foundation of China under Grant 20170520196, in part by the Fundamental Research Funds for the Central Universities under Grant 3132019205, and in part by the Fundamental Research Funds for the Central Universities under Grant 3132019354.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhou, J., Yang, T. & Zhang, W. Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends. Appl Intell 53, 3594–3621 (2023). https://doi.org/10.1007/s10489-022-03767-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-022-03767-y