Skip to main content

Advertisement

SpringerLink
Log in

Underwater image enhancement: a comprehensive review, recent trends, challenges and applications

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

The mysteries of deep-sea ecosystems can be unlocked to reveal new sources, for developing medical drugs, food and energy resources, and products of renewable energy. Research in the area of underwater image processing has increased significantly in the last decade. This is primarily due to the dependence of human beings on the valuable resources existing underwater. Effective work of exploring the underwater environment is achievable by having excellent methods for underwater image enhancement. The work presented in this article highlights the survey of underwater image enhancement algorithms. This work presents an overview of various underwater image enhancement techniques and their broad classifications. The methods under each classification are briefly discussed. Underwater datasets required for performing experiments are summarized from the available literature. Attention is also drawn towards various evaluation metrics required for the quantitative assessment of underwater images and recent areas of application in the domain.

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

Similar content being viewed by others

References

  • AbuNaser A, Doush IA, Mansour N, Alshattnawi S (2015) Underwater image enhancement using particle swarm optimization. J Intell Syst 24(1):99–115

    Article  Google Scholar 

  • Ahn J, Yasukawa S, Sonoda T, Ura T, Ishii K (2017) Enhancement of deep-sea floor images obtained by an underwater vehicle and its evaluation by crab recognition. J Mar Sci Technol 22(4):758–770. https://doi.org/10.1007/s00773-017-0442-1

    Article  Google Scholar 

  • Alex RS, Deepa S, Supriya M (2016) Underwater image enhancement using CLAHE in a reconfigurable platform. In: OCEANS 2016 MTS/IEEE Monterey, IEEE, pp 1–5

  • Ancuti C, Ancuti CO, Haber T, Bekaert P (2012) Enhancing underwater images and videos by fusion. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp 81–88, https://doi.org/10.1109/CVPR.2012.6247661

  • Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2017) Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 27(1):379–393

    Article  MathSciNet  Google Scholar 

  • Anwar S, Li C (2020) Diving deeper into underwater image enhancement: a survey. Signal Process Image Commun 89:115978

    Article  Google Scholar 

  • Ao J, Ma C (2018) Adaptive stretching method for underwater image color correction. Int J Pattern Recognit Artif Intell 32(02):1854001

    Article  MathSciNet  Google Scholar 

  • Au OC, Sun L, Zou R, Dai W, Li S (2012) An improved method for color images enhancement considering HVS. In: 2012 International Conference on Audio. Language and Image Processing, IEEE, pp 117–122

  • Azmi KZM, Ghani ASA, Yusof ZM, Ibrahim Z (2019) Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm. Appl Soft Comput 85:105810

    Article  Google Scholar 

  • Barros W, Nascimento ER, Barbosa WV, Campos MF (2018) Single-shot underwater image restoration: a visual quality-aware method based on light propagation model. J Vis Commun Image Represent 55:363–373. https://doi.org/10.1016/j.jvcir.2018.06.018

    Article  Google Scholar 

  • Basuki A, Ramadijanti N (2016) Improving auto level method for enhancement of underwater images. In: 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC), IEEE, pp 120–125

  • 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. https://doi.org/10.1109/TPAMI.2020.2977624

    Article  Google Scholar 

  • Bindhu A, Maheswari OU (2017) Under water image enhancement based on linear image interpolation and limited image enhancer techniques. In: 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), IEEE, pp 1–5

  • Boom BJ, Huang PX, Beyan C, Spampinato C, Palazzo S, He J, Beauxis-Aussalet E, Lin SI, Chou HM, Nadarajan G et al. (2012) Long-term underwater camera surveillance for monitoring and analysis of fish populations. In: VAIB12

  • Boudhane M, Nsiri B (2016) Underwater image processing method for fish localization and detection in submarine environment. J Vis Commun Image Represent 39:226–238. https://doi.org/10.1016/j.jvcir.2016.05.017

    Article  Google Scholar 

  • Çelebi AT, Ertürk S (2012) Visual enhancement of underwater images using empirical mode decomposition. Expert Syst Appl 39(1):800–805

    Article  Google Scholar 

  • Chang CH, Huang IC, Lin MF, Kuang SR (2017) Design and implementation of a low-cost guided image filter for underwater image enhancement. In: 2017 IEEE Conference on Dependable and Secure Computing, IEEE, pp 296–299

  • Chen Y, Yang K (2013) Map-regularized robust reconstruction for underwater imaging detection. Optik 124(20):4514–4518. https://doi.org/10.1016/j.ijleo.2013.01.053

    Article  Google Scholar 

  • Chen Y, Xia M, Li W, Zhang X, Yang K (2012) Comparison of point spread models for underwater image restoration. Optik 123(9):753–757

    Article  Google Scholar 

  • Chen Z, Wang H, Shen J, Li X, Xu L (2014) Region-specialized underwater image restoration in inhomogeneous optical environments. Optik 125(9):2090–2098. https://doi.org/10.1016/j.ijleo.2013.10.038

    Article  Google Scholar 

  • Chiang JY, Chen Y (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756–1769. https://doi.org/10.1109/TIP.2011.2179666

    Article  MathSciNet  MATH  Google Scholar 

  • 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, Springer, pp 372–383

  • Cui S, Zhou Y, Wang Y, Zhai L (2020) Fish detection using deep learning. Appl Comput Intell Soft Comput. https://doi.org/10.1155/2020/3738108

    Article  Google Scholar 

  • Cui W, Li C, Zhang C, Zhang X (2018) Restoration and enhancement of underwater light field image. In: Wang K, Wang Y, Strandhagen JO, Yu T (eds) Advanced Manufacturing and Automation VII, Springer Singapore, pp 93–105, https://doi.org/10.1016/j.future.2018.01.001

  • Dargan S, Kumar M, Ayyagari MR, Kumar G (2019) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27:1071–1092

    Article  MathSciNet  Google Scholar 

  • Deng X, Wang H, Liu X, Gu Q (2017) State of the art of the underwater image processing methods. In: 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), IEEE, pp 1–6

  • Ding X, Wang Y, Liang Z, Zhang J, Fu X (2018) Towards underwater image enhancement using super-resolution convolutional neural networks. In: Huet B, Nie L, Hong R (eds) Internet Multimedia Computing and Service, Springer, Singapore, pp 479–486, https://doi.org/10.1007/978-981-10-8530-7-47

  • Dixit S, Tiwari SK, Sharma P (2016) Underwater image enhancement using DCP with ACCLAHE and homomorphism filtering. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, pp 2042–2046

  • Duarte A, Codevilla F, Gaya JDO, Botelho SS (2016) A dataset to evaluate underwater image restoration methods. In: OCEANS 2016-Shanghai, IEEE, pp 1–6

  • Dubey N, Tiwari SK, Sharma P (2017) A hybrid DCT-DHE approach for enhancement of low contrast underwater images. In: 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE), IEEE, pp 304–309

  • Dwivedi PK, Paul B, Ghoshal D (2015) Notice of retraction underwater image enhancement using distance factor estimation. In: 2015 International Conference on Electrical. Electronics, Signals, Communication and Optimization (EESCO), IEEE, pp 1–5

  • Emberton S, Chittka L, Cavallaro A (2018) Underwater image and video dehazing with pure haze region segmentation. In: Computer Vision and Image Understanding, Special Issue on Vision and Computational Photography and Graphics 168:145 – 156, https://doi.org/10.1016/j.cviu.2017.08.003

  • Erat O, Panetta K, Agaian S (2017) Contrast enhancement for underwater images in maritime border protection. In: 2017 IEEE International Symposium on Technologies for Homeland Security (HST), IEEE, pp 1–5

  • Farhadifard F, Zhou Z, von Lukas UF (2015) Learning-based underwater image enhancement with adaptive color mapping. In: 2015 9th International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE, pp 48–53

  • Fatan M, Daliri MR, Shahri AM (2016) Underwater cable detection in the images using edge classification based on texture information. Measurement 91:309–317. https://doi.org/10.1016/j.measurement.2016.05.030

    Article  Google Scholar 

  • Fu X, Cao X (2020) Underwater image enhancement with global-local networks and compressed-histogram equalization. Signal Processing: Image Communication 86:115892

    Google Scholar 

  • Fu X, Fan Z, Ling M, Huang Y, Ding X (2017) Two-step approach for single underwater image enhancement. In: 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), IEEE, pp 789–794

  • Galdran A, Pardo D, Picón A, Alvarez-Gila A (2015) Automatic red-channel underwater image restoration. J Vis Commun Image Represent 26:132–145. https://doi.org/10.1016/j.jvcir.2014.11.006

    Article  Google Scholar 

  • Garg D, Garg NK, Kumar M (2018) Underwater image enhancement using blending of CLAHE and percentile methodologies. Multimed Tools Appl 77(20):26545–26561. https://doi.org/10.1007/s11042-018-5878-8

    Article  Google Scholar 

  • Ghani ASA, Isa NAM (2014a) Underwater image quality enhancement through composition of dual-intensity images and rayleigh-stretching. Springerplus 3(1):757

    Article  Google Scholar 

  • Ghani ASA, Isa NAM (2014b) Underwater image quality enhancement through Rayleigh-stretching and averaging image planes. Int J Naval Arch Ocean Eng 6(4):840–866. https://doi.org/10.2478/IJNAOE-2013-0217

    Article  Google Scholar 

  • Ghani ASA, Isa NAM (2015a) Enhancement of low quality underwater image through integrated global and local contrast correction. Appl Soft Comput 37:332–344. https://doi.org/10.1016/j.asoc.2015.08.033

    Article  Google Scholar 

  • Ghani ASA, Isa NAM (2015b) Underwater image quality enhancement through integrated color model with rayleigh distribution. Appl Soft Comput 27:219–230. https://doi.org/10.1016/j.asoc.2014.11.020

    Article  Google Scholar 

  • Ghani ASA, Isa NAM (2017) Automatic system for improving underwater image contrast and color through recursive adaptive histogram modification. Comput Electron Agric 141:181–195. https://doi.org/10.1016/j.compag.2017.07.021

    Article  Google Scholar 

  • Goceri E (2019a) Analysis of deep networks with residual blocks and different activation functions: classification of skin diseases. In: 2019 Ninth international conference on image processing theory, tools and applications (IPTA), IEEE, pp 1–6

  • Goceri E (2019b) Challenges and recent solutions for image segmentation in the era of deep learning. In: 2019 ninth international conference on image processing theory, tools and applications (IPTA), IEEE, pp 1–6

  • Goceri E (2019c) Skin disease diagnosis from photographs using deep learning. In: ECCOMAS thematic conference on computational vision and medical image processing, Springer, pp 239–246

  • González-Rivero M, Beijbom O, Rodriguez-Ramirez A, Bryant DE, Ganase A, Gonzalez-Marrero Y, Herrera-Reveles A, Kennedy EV, Kim CJ, Lopez-Marcano S et al (2020) Monitoring of coral reefs using artificial intelligence: a feasible and cost-effective approach. Remote Sens 12(3):489

    Article  Google Scholar 

  • Guo Q, Xue L, Tang R, Guo L (2017) Underwater image enhancement based on the dark channel prior and attenuation compensation. J Ocean Univ China 16(5):757–765. https://doi.org/10.1007/s11802-017-3242-7

    Article  Google Scholar 

  • Güraksin GE, Köse U, Deperlıoğlu Ö (2016) Underwater image enhancement based on contrast adjustment via differential evolution algorithm. In: 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), IEEE, pp 1–5

  • Guraksin GE, Deperlioglu O, Kose U (2019) A novel underwater image enhancement approach with wavelet transform supported by differential evolution algorithm. Springer, pp 255–278. https://doi.org/10.1007/978-3-319-96002-9-11

  • Han M, Chen C (2016) Enhancing underwater image by dark channel prior and color correction. In: 2016 Sixth International Conference on Information Science and Technology (ICIST), IEEE, pp 505–510

  • Honnutagi P, Mytri VD, Lalitha YS (2019) Fusion-based underwater image enhancement by weight map techniques. In: Kalita J, Balas VE, Borah S, Pradhan R (eds) Recent developments in machine learning and data analytics. Springer, Singapore, pp 327–339. https://doi.org/10.1007/978-981-13-1280-9-31

  • Hou M, Liu R, Fan X, Luo Z (2018) Joint residual learning for underwater image enhancement. In: 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, pp 4043–4047

  • Iqbal K, Salam RA, Osman A, Talib AZ (2007) Underwater image enhancement using an integrated colour model. IAENG Int J Comput Sci 34(2)

  • Iqbal K, Odetayo M, James A, Salam RA, Talib AZH (2010) Enhancing the low quality images using unsupervised colour correction method. In: 2010 IEEE International Conference on Systems, Man and Cybernetics. IEEE, pp 1703–1709

  • 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 

  • Johnsen G, Ludvigsen M, Sørensen A, Aas LMS (2016) The use of underwater hyperspectral imaging deployed on remotely operated vehicles—methods and applications. IFAC-PapersOnLine 49(23): 476–481. In: 10th IFAC Conference on C ontrol Applications in Marine SystemsCAMS 2016. https://doi.org/10.1016/j.ifacol.2016.10.451

  • Khan A, Ali SSA, Malik AS, Anwer A, Meriaudeau F (2016) Underwater image enhancement by wavelet based fusion. In: 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS). IEEE, pp 83–88

  • Khan A, Ali SSA, Anwer A, Adil SH, Mériaudeau F (2018) Subsea pipeline corrosion estimation by restoring and enhancing degraded underwater images. IEEE Access 6:40585–40601

    Article  Google Scholar 

  • Kumar GS, Painumgal UV, Kumar MC, Rajesh K (2018) Autonomous underwater vehicle for vision based tracking. Proc Comput Sci 133:169–180. In: International Conference on Robotics and Smart Manufacturing (RoSMa2018). https://doi.org/10.1016/j.procs.2018.07.021

  • Kumar M, Jindal MK, Sharma RK, Jindal SR (2020) Performance evaluation of classifiers for the recognition of offline handwritten gurmukhi characters and numerals: a study. Artif Intell Rev 53(3):2075–2097

    Article  Google Scholar 

  • Li C, Guo J, Guo C, Cong R, Gong J (2017a) A hybrid method for underwater image correction. Pattern Recogn Lett 94:62–67. https://doi.org/10.1016/j.patrec.2017.05.023

    Article  Google Scholar 

  • Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2019a) An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process 29:4376–4389

    Article  Google Scholar 

  • Li C, Anwar S, Porikli F (2020) Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn 98:10708

    Google Scholar 

  • Li D, Xu L, Liu H (2017b) Detection of uneaten fish food pellets in underwater images for aquaculture. Aquacult Eng 78:85–94. https://doi.org/10.1016/j.aquaeng.2017.05.001

    Article  Google Scholar 

  • Li J, Li Y (2019) Underwater image restoration algorithm for free-ascending deep-sea tripods. Optics Laser Technol 110:129–134. https://doi.org/10.1016/j.optlastec.2018.05.034. Special Issue: Optical Imaging for Extreme Environment

  • Li J, Skinner KA, Eustice RM, Johnson-Roberson M (2017c) Watergan: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot Autom Lett 3(1):387–394

    Google Scholar 

  • Li M, Liu J, Yang W, Sun X, Guo Z (2018a) Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans Image Process 27(6):2828–2841

    Article  MathSciNet  Google Scholar 

  • Li X, Yang Z, Shang M, Hao J (2016a) Underwater image enhancement via dark channel prior and luminance adjustment. In: OCEANS 2016-Shanghai, IEEE, pp 1–5

  • Li Y, Lu H, Li J, Li X, Li Y, Serikawa S (2016b) Underwater image de-scattering and classification by deep neural network. Comput Electr Eng 54:68–77. https://doi.org/10.1016/j.compeleceng.2016.08.008

    Article  Google Scholar 

  • Li Y, Zhang Y, Xu X, He L, Serikawa S, Kim H (2019b) Dust removal from high turbid underwater images using convolutional neural networks. Optics Laser Technol 110:2–6. https://doi.org/10.1016/j.optlastec.2017.09.017. Special Issue: Optical Imaging for Extreme Environment

  • Li Z, Li G, Niu B, Peng F (2018b) Sea cucumber image dehazing method by fusion of retinex and dark channel. IFAC-PapersOnLine 51(17):796–801. https://doi.org/10.1016/j.ifacol.2018.08.098

    Article  Google Scholar 

  • Liu H, Chau LP (2016) Underwater image restoration based on contrast enhancement. In: 2016 IEEE International Conference on Digital Signal Processing (DSP), IEEE, pp 584–588

  • Liu L, Liu B, Huang H, Bovik AC (2014) No-reference image quality assessment based on spatial and spectral entropies. Signal Process Image Commun 29(8):856–863

    Article  Google Scholar 

  • Liu P, Wang G, Qi H, Zhang C, Zheng H, Yu Z (2019) Underwater image enhancement with a deep residual framework. IEEE Access 7:94614–94629

    Article  Google Scholar 

  • Liu R, Fan X, Zhu M, Hou M, Luo Z (2020) Real-world underwater enhancement: Challenges, benchmarks, and solutions under natural light. IEEE Transactions on Circuits and Systems for Video Technology

  • Lu H, Li Y, Xu X, Li J, Liu Z, Li X, Yang J, Serikawa S (2016) Underwater image enhancement method using weighted guided trigonometric filtering and artificial light correction. J Vis Commun Image Represent 38:504–516. https://doi.org/10.1016/j.jvcir.2016.03.029

    Article  Google Scholar 

  • Lu H, Li Y, Zhang Y, Chen M, Serikawa S, Kim H (2017) Underwater optical image processing: a comprehensive review. Mobile Netw Appl 22(6):1204–1211

    Article  Google Scholar 

  • 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 Comput Syst 82:142–148. https://doi.org/10.1016/j.future.2018.01.001

    Article  Google Scholar 

  • Lu J, Li N, Zhang S, Yu Z, Zheng H, Zheng B (2019) Multi-scale adversarial network for underwater image restoration. Optics Laser Technol 110:105–113. https://doi.org/10.1016/j.optlastec.2018.05.048. Special Issue: Optical Imaging for Extreme Environment

  • Mallik S, Khan SS, Pati UC (2017) Visual enhancement of underwater image by white-balanced EMD. 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, pp 1–6

  • Marukatat S (2015) Image enhancement using local intensity distribution equalization. EURASIP J Image Video Process 2015(1):31

    Article  Google Scholar 

  • Mathur M, Goel N (2018) Enhancement of underwater images using white balancing and rayleigh-stretching. In: 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, pp 924–929

  • Mohammadi P, Ebrahimi-Moghadam A, Shirani S (2014) Subjective and objective quality assessment of image: a survey. Majlesi J Electr Eng 9(1):55–83

    Google Scholar 

  • Mohd Azmi KZ, Abdul Ghani AS, Md Yusof Z, Ibrahim Z (2019) Deep underwater image enhancement through colour cast removal and optimization algorithm. Imag Sci J 67(6):330–342

    Article  Google Scholar 

  • Panetta K, Gao C, Agaian S (2015) Human-visual-system-inspired underwater image quality measures. IEEE J Oceanic Eng 41(3):541–551

    Article  Google Scholar 

  • Peng YT, Zhao X, Cosman PC (2015) Single underwater image enhancement using depth estimation based on blurriness. In: 2015 IEEE International Conference on Image Processing (ICIP), IEEE, pp 4952–4956

  • Priyadharsini R, Sree Sharmila T, Rajendran V (2018) A wavelet transform based contrast enhancement method for underwater acoustic images. Multidimension Syst Signal Process 29(4):1845–1859. https://doi.org/10.1007/s11045-017-0533-5

    Article  Google Scholar 

  • Qiao X, Bao J, Zeng L, Zou J, Li D (2017a) An automatic active contour method for sea cucumber segmentation in natural underwater environments. Comput Electron Agric 135:134–142. https://doi.org/10.1016/j.compag.2017.02.008

    Article  Google Scholar 

  • Qiao X, Bao J, Zhang H, Zeng L, Li D (2017b) Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform. Inform Process Agric 4(3):206–213. https://doi.org/10.1016/j.inpa.2017.06.001

    Article  Google Scholar 

  • Qing C, Huang W, Zhu S, Xu X (2015) Underwater image enhancement with an adaptive dehazing framework. In: 2015 IEEE International Conference on Digital Signal Processing (DSP), IEEE, pp 338–342

  • Rodrigues DDdO, de Barros WF, de Queiroz-Neto JP, Fontoura AG, Carvalho JRH (2016) Enhancement of underwater images in low-to-high turbidity rivers. 2016 29th SIBGRAPI Conference on Graphics. Patterns and Images (SIBGRAPI), IEEE, pp 233–240

  • Sankpal S, Deshpande S (2019) Underwater image enhancement by rayleigh stretching with adaptive scale parameter and energy correction. In: Iyer B, Nalbalwar S, Pathak NP (eds) Computing, Communication and Signal Processing. Springer, Singapore, pp 935–947, https://doi.org/10.1007/978-981-13-1513-8-95

  • Sankpal SS, Deshpande SS (2016) Nonuniform illumination correction algorithm for underwater images using maximum likelihood estimation method. J Eng. https://doi.org/10.1155/2016/5718297

    Article  Google Scholar 

  • Sathya R, Bharathi M, Dhivyasri G (2015) Underwater image enhancement by dark channel prior. In: 2015 2nd International Conference on Electronics and Communication Systems (ICECS), IEEE, pp 1119–1123

  • Schettini R, Corchs S (2010) Underwater image processing: state of the art of restoration and image enhancement methods. EURASIP J Adv Signal Process 2010:1–14

    Article  Google Scholar 

  • Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40(1):41–50. https://doi.org/10.1016/j.compeleceng.2013.10.016. 40th-year commemorative issue

  • Sethi R, Sreedevi I, Verma OP, Jain V (2015) An optimal underwater image enhancement based on fuzzy gray world algorithm and bacterial foraging algorithm. 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), IEEE, pp 1–4

  • Shamsuddin N, Ahmad WW, Baharudin B, M Kushairi MR, Mohd F (2012) Significance level of image enhancement techniques for underwater images. In: 2012 International Conference on Computer Information Science (ICCIS), vol 1, pp 490–494. https://doi.org/10.1109/ICCISci.2012.6297295

  • Shigwan SS, Birajdar GK (2015) Objective image quality assessment using perceptual distortion for image retargeting. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp 955–959. https://doi.org/10.1109/NGCT.2015.7375262

  • Shourya S, Kumar S, Jha RK (2016) Adaptive fractional differential approach to enhance underwater images. In: 2016 Sixth International Symposium on Embedded Computing and System Design (ISED), IEEE, pp 56–60

  • Singh G, Jaggi N, Vasamsetti S, Sardana H, Kumar S, Mittal N (2015) Underwater image/video enhancement using wavelet based color correction (WBCC) method. In: 2015 IEEE Underwater Technology (UT), IEEE, pp 1–5

  • Singh R, Biswas M (2016) Adaptive histogram equalization based fusion technique for hazy underwater image enhancement. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), IEEE, pp 1–5

  • Singh R, Biswas M (2017) Contrast and color improvement based haze removal of underwater images using fusion technique. In: 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE, pp 138–143

  • Srividhya K, Ramya M (2015) Performance analysis of pre-processing filters for underwater images. In: 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE), IEEE, pp 1–7

  • Sun X, Liu L, Dong J (2017) Underwater image enhancement with encoding-decoding deep cnn networks. 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications. Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, pp 1–6

  • Tian H, Zhu J, Tan S, Zhang Y, Zhang Y, Li Y, Hou X (2018) Rapid underwater target enhancement method based on polarimetric imaging. Opt Laser Technol 108:515–520. https://doi.org/10.1016/j.optlastec.2018.07.057

    Article  Google Scholar 

  • Treibitz T, Schechner Y (2012) Turbid scene enhancement using multi-directional illumination fusion. IEEE Trans Image Process 21(11):4662–4667. https://doi.org/10.1109/TIP.2012.2208978

    Article  MathSciNet  MATH  Google Scholar 

  • Ueki Y, Ikehara M (2019) Underwater image enhancement based on the iteration of a generalization of dark channel prior. In: 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, pp 1–4

  • Vasamsetti S, Mittal N, Neelapu BC, Sardana HK (2017) Wavelet based perspective on variational enhancement technique for underwater imagery. Ocean Eng 141:88–100. https://doi.org/10.1016/j.oceaneng.2017.06.012

    Article  Google Scholar 

  • Villon S, Mouillot D, Chaumont M, Darling ES, Subsol G, Claverie T, Villéger S (2018) A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Eco Inform 48:238–244. https://doi.org/10.1016/j.ecoinf.2018.09.007

    Article  Google Scholar 

  • 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 

  • Wang Y, Chang R, He B, Liu X, Guo JH, Lendasse A, et al. (2016) Underwater image enhancement strategy with virtual retina model and image quality assessment. In: OCEANS 2016 MTS/IEEE Monterey, IEEE, pp 1–5

  • Wang Y, Ding X, Wang R, Zhang J, Fu X (2017a) Fusion-based underwater image enhancement by wavelet decomposition. In: 2017 IEEE International Conference on Industrial Technology (ICIT), IEEE, pp 1013–1018

  • Wang Y, Liu H, Chau LP (2017b) Single underwater image restoration using adaptive attenuation-curve prior. IEEE Trans Circuits Syst I Regul Pap 65(3):992–1002

    Article  Google Scholar 

  • Wang Y, Li N, Li Z, Gu Z, Zheng H, Zheng B, Sun M (2018) An imaging-inspired no-reference underwater color image quality assessment metric. Comput Electr Eng 70:904–913. https://doi.org/10.1016/j.compeleceng.2017.12.006

    Article  Google Scholar 

  • Wang Y, Song W, Fortino G, Qi LZ, 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 

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  • Wong SL, Paramesran R, Taguchi A (2018) Underwater image enhancement by adaptive gray world and differential gray-levels histogram equalization. Adv Electr Comput Eng 18(2):109–116

    Article  Google Scholar 

  • Xiao J, Hays J, Ehinger KA, Oliva A, Torralba A (2010) Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 3485–3492

  • Yang J, Wang X, Yue H, Fu X, Hou C (2017) Underwater image enhancement based on structure-texture decomposition. In: 2017 IEEE International Conference on Image Processing (ICIP), IEEE, pp 1207–1211

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

    Article  MathSciNet  Google Scholar 

  • 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 

  • Yang M, Hu K, Du Y, Wei Z, Sheng Z, Hu J (2020) Underwater image enhancement based on conditional generative adversarial network. Signal Process Image Commun 81:115723

    Article  Google Scholar 

  • Ye X, Xu H, Ji X, Xu R (2018) Underwater image enhancement using stacked generative adversarial networks. In: Hong R, Cheng WH, Yamasaki T, Wang M, Ngo CW (eds) Advances in Multimedia Information Processing—PCM 2018. Springer International Publishing, pp 514–524. https://doi.org/10.1007/978-3-030-00764-5-47

  • Zhang C, Zhang X, Tu D (2018a) Underwater image enhancement by fusion. In: Wang K, Wang Y, Strandhagen JO, Yu T (eds) Advanced Manufacturing and Automation VII. Springer, Singapore, pp 81–92, 978-981-10-5768-7-8

  • Zhang S, Wang T, Dong J, Yu H (2017a) Underwater image enhancement via extended multi-scale retinex. Neurocomputing 245:1–9. https://doi.org/10.1016/j.neucom.2017.03.029

    Article  Google Scholar 

  • Zhang T, Li Y, Takahashi S (2020) Underwater image enhancement using improved generative adversarial network. Concurrency and Computation: Practice and Experience, p e5841

  • Zhang W, Li G, Ying Z (2017b) A new underwater image enhancing method via color correction and illumination adjustment. In: 2017 IEEE Visual Communications and Image Processing (VCIP), IEEE, pp 1–4

  • Zhang W, Li G, Ying Z (2018b) Underwater image enhancement by the combination of dehazing and color correction. In: Hong R, Cheng WH, Yamasaki T, Wang M, Ngo CW (eds) Advances in Multimedia Information Processing—PCM 2018. Springer International Publishing, pp 145–155, https://doi.org/10.1007/978-3-030-00764-5-14

  • Zhang W, Dong L, Pan X, Zou P, Qin L, Xu W (2019) A survey of restoration and enhancement for underwater images. IEEE Access 7:182259–182279

    Article  Google Scholar 

  • Zhao X, Jin T, Qu S (2015) Deriving inherent optical properties from background color and underwater image enhancement. Ocean Eng 94:163–172. https://doi.org/10.1016/j.oceaneng.2014.11.036

    Article  Google Scholar 

  • Zheng L, Shi H, Sun S (2016) Underwater image enhancement algorithm based on CLAHE and USM. In: 2016 IEEE International Conference on Information and Automation (ICIA), IEEE, pp 585–590

  • Zhou Y, Li Q, Huo G (2017) Underwater moving target detection based on image enhancement. In: Cong F, Leung A, Wei Q (eds) Advances in Neural Networks—ISNN 2017. Springer International Publishing, pp 427–436

  • Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Graphics Gems, pp 474–485

Download references

Acknowledgements

We are thankful to the reviewers and editors for their valuable suggestions. We appreciate the insightful comments, which helped us in improving the manuscript significantly.

Funding

No funding was received to assist with the preparation of this manuscript

Author information

Authors and Affiliations

  1. Department of Electronics Engineering, Ramrao Adik Institute of Technology, Navi Mumbai, 400706, India

    Smitha Raveendran & Gajanan K. Birajdar

  2. Department of Electronics and Telecommunication Engineering, Ramrao Adik Institute of Technology, Navi Mumbai, 400706, India

    Mukesh D. Patil

Authors
  1. Smitha Raveendran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukesh D. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gajanan K. Birajdar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gajanan K. Birajdar.

Ethics declarations

Conflicts of interest

The authors declare that they have 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raveendran, S., Patil, M.D. & Birajdar, G.K. Underwater image enhancement: a comprehensive review, recent trends, challenges and applications. Artif Intell Rev 54, 5413–5467 (2021). https://doi.org/10.1007/s10462-021-10025-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-021-10025-z

Keywords

Search

Navigation