Skip to main content
SpringerLink
Log in

Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends

  • Published:
Applied Intelligence Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. 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

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. Schechner YY, Karpel N (2005) Recovery of underwater visibility and structure by polarization analysis. IEEE J Ocean Eng 30(3):570–587

    Article  Google Scholar 

  9. Treibitz T, Schechner YY (2006) Instant 3Descatter. In: Proc. CVPR, New York, USA, pp 1861–1868

  10. Treibitz T, Schechner YY (2009) Active polarization descattering. IEEE Trans Pattern Anal Mach Intell 31(3):385–399

    Article  Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. Wu HD, Zhao M, Xu WH (2020) Underwater de-scattering imaging by laser field synchronous scanning. Opt Lasers Eng 126:1–8

    Article  Google Scholar 

  15. Ishibashi S (2011) The study of the underwater camera model. In: Proc. OCEANS, Santander, Spain, pp 1–6

  16. Pizerr SM, Amburn EP, Austin JD et al (1987) Adaptive histogram equalization and its variations. Comput Gr Image Process 39(3):355–368

  17. 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

    Article  Google Scholar 

  18. Roser M, Dunbabin M, Geiger A (2014) Simultaneous underwater visibility assessment enhancement and improved stereo. In: Proc. ICPA, Hong Kong, China, pp 3840–3847

    Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. Tan CS, Sluzek A, Seet G, Jiang TY (2006) Range gated imaging system for underwater robotic vehicle. In: Proc. OCEANS, Singapore, pp 1–6

  22. 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

    Google Scholar 

  23. 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

    Google Scholar 

  24. 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

    Article  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. Han P, Liu F, Wei Y, Shao X (2020) Optical correlation assists to enhance underwater polarization imaging performance. Opt Lasers Eng 134:1–6

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. McGlamery BL (1980) A computer model for underwater camera systems. In: Proc. SPIE, Monterey, USA, pp 221–231

    Google Scholar 

  30. Jaffe JS (1990) Computer modeling and the design of optimal underwater imaging systems. IEEE J Ocean Eng 15(2):101–111

    Article  Google Scholar 

  31. 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

    Article  Google Scholar 

  32. Fattal R (2008) Single image dehazing. ACM Trans Graph 27(3):1–9

    Article  Google Scholar 

  33. Tan RT (2008) Visibility in bad weather from a single image. In: Proc. CVPR, Anchorage, AK, USA, pp 1–8

    Google Scholar 

  34. 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

    Article  Google Scholar 

  35. Chiang JY, Chen Y (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756–1769

    Article  MathSciNet  MATH  Google Scholar 

  36. 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

    Google Scholar 

  37. 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

    Google Scholar 

  38. Galdran A, Pardo D, Picòn A, Álvarez-Gila A (2015) Automatic red-channel underwater image restoration. J Vis Commun Image Represent 26:132–145

    Article  Google Scholar 

  39. 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

    Article  MathSciNet  MATH  Google Scholar 

  40. 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

    Article  Google Scholar 

  41. Lee HS, Moon SW, Eom IK (2020) Underwater image enhancement using successive color correction and superpixel dark channel prior. Symmetry 12(8):1–18

    Article  Google Scholar 

  42. Carlevaris-Bianco N, Mohan A, Eustice RM (2010) Initial results in underwater single image dehazing. In: Proc. OCEANS, Seattle, USA, pp 1–8

    Google Scholar 

  43. 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

    Google Scholar 

  44. Peng Y, Cosman P (2017) Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 26(4):1579–1594

    Article  MathSciNet  MATH  Google Scholar 

  45. Emberton S, Chittka L, Cavallaro A (2015) Hierarchical rank-based veiling light estimation for underwater dehazing. In: Proc. BMVC, Swansea, UK, pp 1–12

    Google Scholar 

  46. 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

  47. Peng Y, Cosman P (2016) Single image restoration using scene ambient light differential. In: Proc. ICIP, Phoenix, pp 1953–1957

    Google Scholar 

  48. Ancuti CO, Ancuti C, Vleeschouwer CD, Garcia R, Bovik AC (2016) Multi-scale underwater descattering. In: Proc. ICPR, Cancun, pp 4202–4207

    Google Scholar 

  49. 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

    Article  MathSciNet  MATH  Google Scholar 

  50. 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

    Google Scholar 

  51. 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

    Google Scholar 

  52. Cho Y, Shin Y, Kim A (2016) Online depth estimation and application to underwater image dehazing. In: Proc. OCEANS. Monterey, CA, pp 1–7

    Google Scholar 

  53. 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

    Article  Google Scholar 

  54. Drews P, Nascimento ER, Campos MFM, Elfes A (2015) Automatic restoration of underwater monocular sequences of images. In: Proc. IROS, Hamburg, pp 1058–1064

    Google Scholar 

  55. 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

    Google Scholar 

  56. Emberton S, Chittka L, Cavallaro A (2018) Underwater image and video dehazing with pure haze region segmentation. Comput Vis Image Underst 168:145–156

    Article  Google Scholar 

  57. Xu L, Ren JS, Liu C, Jia J (2014) Deep convolutional neural network for image deconvolution. In: Proc. NIPS, MA, USA, pp 1790–1798

    Google Scholar 

  58. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. CVPR, Las Vegas, pp 770–778

    Google Scholar 

  59. 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

    Article  MathSciNet  MATH  Google Scholar 

  60. Fu XY, Cao XY (2020) Underwater image enhancement with global-local networks and compressed-histogram equalization. Signal Process-Image Commun 86:1–15

    Article  Google Scholar 

  61. Raihan J, Abas PE, Silva LCD (2019) Review of underwater image restoration algorithms. IET Signal Process 13(10):1587–1596

    Google Scholar 

  62. 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

    Google Scholar 

  63. 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

    Google Scholar 

  64. 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

    Google Scholar 

  65. Li C, Anwar S, Porikli F (2020) Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn 98(1):1–11

    Google Scholar 

  66. 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

    Article  Google Scholar 

  67. 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

    Article  Google Scholar 

  68. 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

    Google Scholar 

  69. 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

    Article  MATH  Google Scholar 

  70. 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

    Google Scholar 

  71. 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

    Article  Google Scholar 

  72. Fabbri C, Islam MJ, Sattar J (2018) Enhancing underwater imagery using generative adversarial networks. In: Proc. ICRA, Brisbane, QLD, Australia, pp 7159–7165

    Google Scholar 

  73. 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

    Google Scholar 

  74. 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

    Google Scholar 

  75. 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

    Article  Google Scholar 

  76. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Proc. Graphics Gems IV. Academic Press Professional, pp 474–485

    Google Scholar 

  77. 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

  78. 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

    Article  Google Scholar 

  79. Ancuti C, Ancuti CO, Haber T, Bekaert P (2012) Enhancing underwater images and videos by fusion. In: Proc. CVPR, Providence, USA, pp 81–88

    Google Scholar 

  80. 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

    Article  MathSciNet  MATH  Google Scholar 

  81. 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

    Article  Google Scholar 

  82. Song HJ, Wang R (2021) Underwater image enhancement based on multi-scale fusion and global stretching of dual-model. Mathematics 9(6):1–14

    Article  Google Scholar 

  83. Jobson DJ, Rahman Z, Woodell GA (1997) Properties and performance of a center/surround retinex. IEEE Trans Image Process 6(3):451–462

    Article  Google Scholar 

  84. 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

    Article  Google Scholar 

  85. Zhang S, Wang T, Dong JY, Yu H (2017) Underwater image enhancement via extended multi-scale Retinex. Neurocomputing 245:1–9

    Article  Google Scholar 

  86. 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

    Article  Google Scholar 

  87. 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

    Google Scholar 

  88. 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

    Article  Google Scholar 

  89. Akkaynak D, Treibitz T (2019) Sea-Thru: a method for removing water from underwater images. In: Proc. CVPR, pp 1682–1691

    Google Scholar 

  90. Islam MJ, Xia Y, Sattar J (2020) Fast underwater image enhancement for improved visual perception. IEEE Robot Autom Lett 5(2):3227–3234

    Article  Google Scholar 

  91. 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

    Article  Google Scholar 

  92. 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

    Google Scholar 

  93. 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

    Google Scholar 

  94. Panetta K, Gao C, Agaian S (2016) Human-visual-system-inspired underwater image quality measures. IEEE J Ocean Eng 41(3):541–551

    Article  Google Scholar 

  95. Yang M, Sowmya A (2015) An underwater color image quality evaluation metric. IEEE Trans Image Process 24(12):6062–6071

    Article  MathSciNet  MATH  Google Scholar 

  96. 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

    Article  Google Scholar 

  97. 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

    Article  Google Scholar 

  98. 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

    Article  Google Scholar 

  99. 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

    Google Scholar 

  100. Akkaynak D, Treibitz T (2018) A revised underwater image formation model. In: Proc. CVPR, pp 6723–6732

    Google Scholar 

  101. Yang L, Chao X, Ercisli S (2022) Disturbed-entropy: a simple data quality assessment approach. ICT Express:1–4

  102. Yang L, Yang J, Wen J (2021) Entropy-based redundancy analysis and information screening. Digit Commun Netw:1–11

  103. Yang L, Chao X (2022) Distance-entropy: an effective Indicator for selecting informative data. Front Plant Sci 12:1–8

    Google Scholar 

  104. 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

    Article  Google Scholar 

Download references

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

  1. College of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China

    Jingchun Zhou, Tongyu Yang & Weishi Zhang

Authors
  1. Jingchun Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tongyu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weishi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jingchun Zhou or Weishi Zhang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03767-y

Keywords

Search

Navigation