Skip to main content
Springer Nature Link
Log in

A systematic review of the methodologies for the processing and enhancement of the underwater images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Underwater image processing has received tremendous attention in the past few years. The reason for increased research in this area is that the process of taking images underwater is very difficult. Images obtained underwater frequently suffer from quality deterioration issues such as poor contrast, blurring features, colour variations, non-uniform lighting, the presence of dust particles, noise at the bottom of the sea, different properties of the water medium, and so on. The improvement of underwater images is a critical problem in image processing and computer vision for a variety of practical applications. To address this problem, we need to find some other methods to increase the quality of the image while capturing it underwater. But capturing the image in normal circumstances as well as underwater is the same, so once we get an image, some mechanism to increase the quality of the captured image will also be required. A complete and in-depth study of relevant accomplishments and developments, particularly the survey of underwater image methods and datasets, which are a critical issue in underwater image processing and intelligent application, is still lacking. In this paper, we first provide a review of more than 85 articles on the most recent advancements in underwater image restoration methods, underwater image enhancement methods, and underwater image enhancement using deep learning and machine learning methods, along with the techniques, data sets, and evaluation criteria. To provide a thorough grasp of underwater image restoration, enhancement, and enhancement using deep learning and machine learning, we explore the strengths and limits of existing techniques. Additionally, we offer thorough, unbiased reviews and evaluations of the representative methodologies for five distinct types of underwater situations, which vary their usefulness in various underwater circumstances. Two main evaluations, subjective image quality evaluation and objective image quality evaluation; are used for evaluating the quality of images. These evaluations are useful to determine the efficiency of the predefined methods. With the help of these image quality evaluations, we come to the conclusion that the image enhancement methods and image enhancement methods using deep learning and machine learning are superior in comparison to the image restoration methods. As deep learning and machine learning based enhancement methods are newer and give far better results in comparison to the other two methods, lots of researchers are moving towards these methods. Finally, we also explore the potential difficulties and unresolved problems associated with underwater image enhancement and offer potential future research areas.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Almabouada F, Abreu MA, Coelho JMP, Aiadi KE (2019) Experimental and simulation assessments of underwater light propagation. Front Optoelectron 12(4):405–412

    Article  Google Scholar 

  2. Ancuti C, Ancuti CO, Haber T et al (2012) Enhancing underwater images and videos by fusion. Proceeding IEEE conference on computer vision and pattern recognition (CVPR 2012) pp 81–88

  3. Ancuti C, Ancuti CO, De C et al (2016) Multi-scale underwater descattering. Procceding of 23rd international conference on pattern recognition (ICPR 2016) pp 4202–4207

  4. Ancuti CO, Ancuti C, De C et al (2017) Color transfer for underwater dehazing and depth estimation. Proceeding IEEE international conference on image processing (2017) pp 695–699

  5. Ancuti CO, Ancuti C, De C et al (2017) Locally adaptive color correction for underwater image dehazing and matching, proceeding on IEEE computer vision and pattern recognition. Workshops pp 1–9

  6. Ancuti CO, Ancuti C, De Vleeschouwer C et al (2018) Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 27(1):379–393

    Article  MathSciNet  MATH  Google Scholar 

  7. Anwar S, Li C (2020) Diving deeper into underwater image enhancement: A survey. Signal Process Image Commun 89:115,978–115,978

    Article  Google Scholar 

  8. Arnold-Bos A, Malkasse JP, Kervern G (2005) A preprocessing framework for automatic underwater images denoising, european conference on propagation and systems. pp 15–18

  9. Bazeille S, Quidu I, Jaulin L et al (2006) Automatic underwater image pre-processing. Proceeding Caracterisation Du Milieu Marin. pp 16–19

  10. Berman D, Levy D, Avidan S et al (2018) Underwater single image color restoration using haze-lines and a new quantitative dataset. URL: https://arxiv.org/abs/1811.01343

  11. Boom BJ, He J, Palazzo S, Huang PX, Beyan C, Chou HM, Lin FP, Spampinato C, Fisher RB (2014) A research tool for long term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage. Ecol Inform 23:83–97

    Article  Google Scholar 

  12. Bryson M, Johnson-Roberson M, Pizarro O, Williams SB (2016) True color correction of autonomous underwater vehicle imagery. J Field Robot 33(6):853–874

    Article  Google Scholar 

  13. Caimi FM, Dalgleish FR, Giddings TE et al (2007) Pulse versus CW laser line scan imaging detection methods: simulation results. Europe 18:1–4

    Google Scholar 

  14. Carlevaris-Bianco N, Mohan A, Eustice RM (2010) Initial results in underwater single image dehazing, Oceans Mts/IEEE Seattle pp 1–8

  15. Chambah M, Semani D, Renouf A et al (2003) Underwater color constancy: enhancement of automatic live fish recognition, Color Imaging IX: Processing, Hardcopy, and Applications pp 157–168

  16. Chen CLP, Zhou J, Zhao W (2012) A real-time vehicle navigation algorithm in sensor network environments. IEEE Trans Intell Transp Syst 13(4):1657–1666

    Article  Google Scholar 

  17. Chiang JY, Chen YC (2011) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756–1769

    Article  MathSciNet  MATH  Google Scholar 

  18. Chiang JY, Chen YC, Chen YF (2011) Underwater image enhancement: using wavelength compensation and image dehazing (WCID). In International conference on advanced concepts for intelligent vision systems pp 372–383

  19. Cho Y, Shin YS, Kim A (2016) Online depth estimation and application to underwater image dehazing. Proceeding of IEEE Oceans pp 1–7

  20. Cutter G, Stierhoff K, Zeng J et al (2015) Automated detection of rocks in unconstrained underwater videos using Haar cascades and a new image dataset: labelled fishes in the wild. IEEE Winter Appl Comput Vis Workshops 1:57–62

    Google Scholar 

  21. CVPR 2018 Workshop and Challenge (AAMVEM) (n.d.) Available: http://www.viametoolkit.org/cvpr-2018-workshop-data-challenge/challenge-data-description/

  22. CVPR 2019 Workshop and Challenge (AAMVEM) (n.d.) Available: https://www.aamvem.com/datachallenge

  23. Del Grosso VA (1975) Modulation transfer function of water. Oceans 75 Conference pp 331–347

  24. Ding X, Wang Y, Zhang J (2017) Underwater image dehaze using scene depth estimation with adaptive color correction, Procceding of IEEE Oceans pp 1–5

  25. Drews P, Nascimento E, Moraes F et al (2013) Transmission estimation in underwater single images. Proceedings of the IEEE international conference on computer vision workshops pp 815–830

  26. Emberton S, Chittka L, Cavallaro A (2015) Hierarchical rank-based veiling light estimation for underwater dehazing. Proceedings of the British machine vision conference pp 1–12

  27. 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 

  28. Fish4Knowledge (n.d.) Available: http://homepages.nf.ed.ac.uk/rbf/Fish4Knowledge/?tdsourcetag=s_pcqq_aiomsg.

  29. Fu X, Zhuang P, Huang Y et al (2014) A Retinex-based enhancing approach for single underwater image. Proceeding IEEE international conference on image processing, (ICIP 2014) pp 4572–4576

  30. Fu X, Zhuang P, Huang Y et al (2014) A retinex-based enhancing approach for single underwater image. IEEE international conference on image processing (ICIP 2014) pp 4572–4576

  31. Galdran A, Pardo D, Picón A et al (2015) Automatic red channel underwater image restoration. J Vis Commun Image Represent 26:132–145

    Article  Google Scholar 

  32. Ghani ASA, Isa NAM (2014) Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching. Springer Plus 3(1):757

    Article  Google Scholar 

  33. Ghani ASA, Isa NAM (2015) Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl Soft Comput 27:219–230

    Article  Google Scholar 

  34. Goodfellow I, Pouget-Abadie J, Mirza M et al Generative adversarial nets. Proceeding International Conference on Neural Information Processing Systems pp 2672–2680

  35. Grosso VAD (1978) Optical transfer function measurements in the Sargasso Sea. International Society for Optics and Photonics pp 74–101

  36. HabCam Underwater Image Dataset. (n.d.) Available: https://habcam.whoi.edu/

  37. Han HW, Zhang XH, Ge WL et al (2011) A mixed noise reduction algorithm for underwater laser images based on soft-morphological filter. Acta Photonica Sinica 40(1):136–141

    Article  Google Scholar 

  38. 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 

  39. Honjo S, Doherty KW, Agrawal YC (1984) Direct optical assessment of large amorphous aggregates (marine snow) in the deep ocean. Deep Sea Res Part A Oceanogr Res Pap 31(1):67–76

    Article  Google Scholar 

  40. Hou W, Gray DJ, Weidemann AD (2007) Automated underwater image restoration and retrieval of related optical properties. IEEE International Geoscience and Remote Sensing Symposium pp 1889–1892

  41. Hou W, Weidemann D, Gray DJ (2007) Imagery derived modulation transfer function and its applications for underwater imaging. Int Soc Optics Photonics 6696:707–714

    Google Scholar 

  42. Hou W, Gray DJ, Weidemann AD, Arnone RA (2008) Comparison and validation of point spread models for imaging in natural waters. Opt Express 16(13):9958–9965

    Article  Google Scholar 

  43. Hou M, Liu R, Fan X et al (2018) Joint residual learning for underwater image enhancement. Proceeding IEEE international conference on image processing pp 4043–4047

  44. Hufnagel RE, Stanley NR (1964) Modulation transfer function associated with image transmission through turbulent media. J Opt Soc Am 54(1):52–60

    Article  Google Scholar 

  45. Iqbal K, Salam RA, Osman A et al (2007) Underwater image enhancement using an integrated color model. Int J Comput Sci 34(2):1–6

    Google Scholar 

  46. Jia DX, Ge YR (2012) Underwater image de-noising algorithm based on non sub sampled contour let transform and total variation. International conference on computer science and information processing (CSIP 2012) pp 76–80

  47. Jian M, Qi Q, Dong J et al (2017) The OUCvision large-scale underwater image database. IEEE international conference on multimedia and expo (ICME 2017) pp 1297–1302

  48. Li C, Guo J, Chen S et al (2016) Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging, proceeding IEEE international conference on image processing, (ICIP 2016) pp 1993–1997

  49. Li C, Guo J, Guo C, Cong R, Gong J (2017) A hybrid method for underwater image correction. Pattern Recogn Lett 94:62–67

    Article  Google Scholar 

  50. Li J, Skinner KA, Eustice RM et al (2018) Water-GAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot Autom Lett 3(1):387–394

    Google Scholar 

  51. Li C, Guo J, Guo C et al (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 

  52. Li Y, Takahashi S, Serikawa S (2019) Cognitive Ocean of things: a comprehensive review and future trends. Wirel Netw. pp 1–10

  53. 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 

  54. Liu R, Fan X, Zhu M et al (2019) Real-world underwater enhancement: Challenges, benchmarks, and solutions, (2019), Available: https://arxiv.org/abs/1901.05320

  55. MBARI Underwater Image Dataset (n.d.) Available: https://www.mbari.org

  56. McGlamery BL (1980) A computer model for underwater camera systems. International Society for Optics and Photonics (208)

  57. Nery MS, Machado AM, Campos MFM et al (2005) Determining the appropriate feature set for fish classification tasks. XVIII Brazilian symposium on computer graphics and image processing (SIBGRAPI'05) pp 173–180

  58. Peng YT, Cosman PC (2016) Single image restoration using scene ambient light differential. Proceeding IEEE international conference on image processing (ICIP 2016) pp 1953–1957

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

    Article  MathSciNet  MATH  Google Scholar 

  60. Peng YT, Zhao X, Cosman PC (2015) Single underwater image enhancement using depth estimation based on blurriness. Proceeding IEEE international conference on image processing pp 4952–4956

  61. Perez J, Attanasio AC, Nechyporenko N et al (2017) A deep learning approach for underwater image enhancement. International work-conference on the interplay between natural and artificial computation pp 183–192

  62. Petit F, Capelle-Laize AS, Carre P (2009) Underwater image enhancement by attenuation inversion with quaternions. IEEE international conference on acoustics, speech and signal processing, (ICASSP 2009) pp 1177–1180

  63. Port Royal Underwater Image Database and Underwater Rock Image Database (n.d.) Available: https://github.com/kskin/WaterGAN/

  64. Qin D, Yue G, Yeqiu L, Le Z et al (2013) High-precision measurement technology of laser pulse flight time based on TDC-GP21. Infrared Laser Eng 42(7):1706–1709

    Google Scholar 

  65. Rai KR, Gour P, Singh B (2012) Underwater image segmentation using CLAHE enhancement and thresholding. Int J Emerg Technol Adv Eng 2(1):118–123

    Google Scholar 

  66. RGBD Underwater Image Dataset (n.d.) Available: http://csms.haifa.ac.il/proles/tTreibitz/datasets/ambient_forwardlooking/index.html

  67. RUIE Dataset (n.d.) Available: https://github.com/dlut-dimt/Realworld-Underwater-Image-Enhancement-RUIEBenchmark

  68. Sahoo A, Dwivedy SK, Robi PS (2019) Advancements in the field of autonomous underwater vehicle. Ocean Eng 181:145–116

    Article  Google Scholar 

  69. Singh N, Bhat A (2021) A detailed understanding of underwater image enhancement using deep learning. In: 2021 5th International Conference on Information Systems and Computer Networks (ISCON 2021). IEEE, pp 1–6

  70. Soni OK, Kumar JS et al (2020) A survey on underwater images enhancement techniques. IEEE 9th international conference on communication systems and network technologies (CSNT 2020) pp 333-338

  71. Tan CS, Sluzek A, G. Seet GL, Jiang TY (2006) Range gated imaging system for underwater robotic vehicle. In: OCEANS 2006-Asia Pacific. IEEE, pp 1–6

  72. Torres-Méndez LA, Dudek G (2005) Color correction of underwater images for aquatic robot inspection. In: Energy Minimization Methods in Computer Vision and Pattern Recognition: 5th International Workshop, EMMCVPR 2005, St. Augustine, FL, USA, November 9-11, 2005. Proceedings 5. Springer Berlin Heidelberg, pp 60–73

  73. Trucco E, Olmos-Antillon AT (2006) Self-tuning underwater image restoration. IEEE J Ocean Eng 31(2):511–519

    Article  Google Scholar 

  74. Uemura T, Lu H, Kim H (2020) Marine organisms tracking and recognizing using yolo. In: 2nd EAI International Conference on Robotic Sensor Networks: ROSENET 2018. Springer International Publishing, pp 53–58

  75. Underwater Photography Fish Database (n.d.) Available: http://www.fishdb.co.uk/. Accessed 27 Aug 2019

  76. Vasamsetti S, Mittal N, Neelapu BC, Sardana HK (2017) Wavelet based perspective on variational enhancement technique for underwater imagery. Ocean Eng 141:88–100

    Article  Google Scholar 

  77. Voss KJ, Chapin AL (1990) Measurement of the point spread function in the ocean. Appl Opt 29(25):3638–3642

    Article  Google Scholar 

  78. Wang M, Wu Y, Li J et al (2010) Object recognition via adaptive multi-level feature integration. 12th international Asia-Pacific web conference pp 253–259

  79. Wang K, Dunn E, Tighe, Frahm JM (2014) Combining scene priors and haze removal for single image depth estimation. In: IEEE Winter Conference on Applications of Computer Vision. IEEE, pp 800–807

  80. Wang Y, Liu H, Chau LP (2017) Single underwater image restoration using attenuation-curve prior. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, pp 1–4

  81. Wang Y, Liu H, Chau LP (2018) Single underwater image restoration using adaptive attenuation-curve prior. IEEE Trans Circuits Syst I Reg Pap 65(3):992–1002

    Article  Google Scholar 

  82. Yang M, Gong C et al (2012) Underwater image restoration by turbulence model based on image gradient distribution. 2nd international conference on uncertainty reasoning and knowledge engineering pp 296–299

  83. Yang M, Sowmya A, Wei Z et al (2019) Offshore underwater image restoration using reaction-decomposition-based transmission map estimation. IEEE J Ocean Eng 45(2):521–533

    Article  Google Scholar 

  84. Yang M, Hu J, Li C, Rohde G, du Y, Hu K (2019) An in-depth survey of underwater image enhancement and restoration. IEEE Access 7:123638–123657

    Article  Google Scholar 

  85. Zhu JY, Park T, Isola P et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceeding IEEE international conference on computer vision (ICCV 2017) pp 2223–2232

Download references

Funding

Any agency does not fund this study.

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Delhi Technological University, Delhi, Delhi, 110042, India

    Nishant Singh & Aruna Bhat

Authors
  1. Nishant Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Aruna Bhat
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Aruna Bhat.

Ethics declarations

Author 1 declares that he/she has no conflict of interest.

Author 2 declares that he/she has no conflict of interest.

This article does not contain any studies with human participants or animals performed by any of the authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

All the authors of this paper declare that he/she has no conflict of interest.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, N., Bhat, A. A systematic review of the methodologies for the processing and enhancement of the underwater images. Multimed Tools Appl 82, 38371–38396 (2023). https://doi.org/10.1007/s11042-023-15156-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15156-9

Keywords