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Received: 2020-04-23

Revision Accepted: 2020-07-12

Crosschecked: 2020-11-11

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Jing-chun Zhou


Wei-shi Zhang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.12 P.1745-1769


Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey

Author(s):  Jing-chun Zhou, De-huan Zhang, Wei-shi Zhang

Affiliation(s):  College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

Corresponding email(s):   zhoujingchun@dlmu.edu.cn, zhangdehuan@dlmu.edu.cn, teesiv@dlmu.edu.cn

Key Words:  Underwater image defogging, Restoration approaches, Enhancement approaches, Evaluation metrics

Jing-chun Zhou, De-huan Zhang, Wei-shi Zhang. Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1745-1769.

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T1 - Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey
A1 - Jing-chun Zhou
A1 - De-huan Zhang
A1 - Wei-shi Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000190

In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift, low contrast, noise, and blurring issues. This paper aims to classify and compare some of the significant technologies in underwater image defogging, presenting a comprehensive picture of the current research landscape for researchers. First we analyze the reasons for degradation of underwater images and the underwater optical imaging model. Then we classify the underwater image defogging technologies into three categories, including image restoration approaches, image enhancement approaches, and deep learning approaches. Afterward, we present the objective evaluation metrics and analyze the state-of-the-art approaches. Finally, we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Abu A, Diamant R, 2019. Unsupervised local spatial mixture segmentation of underwater objects in sonar images. IEEE J Ocean Eng, 44(4):1179-1197.

[2]Alex RSM, Supriya MH, 2015. Underwater image enhancement using single scale retinex on a reconfigurable hardware. Int Symp on Ocean Electronics, p.1-5.

[3]Amer KO, Elbouz M, Alfalou A, 2019. Enhancing underwater optical imaging by using a low-pass polarization filter. Opt Expr, 27(2):621-643.

[4]Ancuti C, Ancuti CO, Haber T, et al., 2012. Enhancing underwater images and videos by fusion. IEEE Conf on Computer Vision and Pattern Recognition, p.81-88.

[5]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.

[6]Anwar S, Li CY, 2020. Diving deeper into underwater image enhancement: a survey. Signal Process Image Commun, 89:115978.

[7]Azmi KZM, Ghani ASA, Yusof ZM, et al., 2019. Natural- based underwater image color enhancement through fusion of swarm-intelligence algorithm. Appl Soft Comput, 85:105810.

[8]Bai L, Zhang W, Pan X, et al., 2020. Underwater image enhancement based on global and local equalization of histogram and dual-image multi-scale fusion. IEEE Access, 8:128973-128990.

[9]Buchsbaum G, 1980. A spatial processor model for object colour perception. J Franklin Inst, 310(1):1-26.

[10]Cai BL, Xu XM, Jia K, et al., 2016. DehazeNet: an end-to-end system for single image haze removal. IEEE Trans Image Process, 25(11):5187-5198.

[11]Cai WW, Wei ZG, 2020. PiiGAN: generative adversarial networks for pluralistic image inpainting. IEEE Access, 8:48451-48463.

[12]Carlevaris-Bianco N, Mohan A, Eustice RM, 2010. Initial results in underwater single image dehazing. OCEANS 2010 MTS/IEEE SEATTLE, p.1-8.

[13]Chang YK, Jung CL, Ke P, et al., 2018. Automatic contrast- limited adaptive histogram equalization with dual Gamma correction. IEEE Access, 6:11782-11792.

[14]Chao L, Wang M, 2010. Removal of water scattering. Proc 2nd Int Conf on Computer Engineering and Technology, p.35-39.

[15]Chen XY, Yu JZ, Kong SH, et al., 2019. Towards real-time advancement of underwater visual quality with GAN. IEEE Trans Ind Electron, 66(12):9350-9359.

[16]Chiang JY, Chen YC, 2012. Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process, 21(4):1756-1769.

[17]Dai CG, Lin MX, Wu XJ, et al., 2020. Single underwater image restoration by decomposing curves of attenuating color. Opt Laser Technol, 123:105947.

[18]Demirel H, Anbarjafari G, 2011. IMAGE resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans Image Process, 20(5):1458-1460.

[19]Deng G, 2011. A generalized unsharp masking algorithm. IEEE Trans Image Process, 20(5):1249-1261.

[20]Deng XY, Wang HG, Liu X, 2019. Underwater image enhancement based on removing light source color and dehazing. IEEE Access, 7:114297-114309.

[21]Ding XY, Wang YF, Zhang J, et al., 2017. Underwater image dehaze using scene depth estimation with adaptive color correction. OCEANS Aberdeen, p.1-5.

[22]Drews PJr, Do Nascimento E, Moraes F, et al., 2013. Transmission estimation in underwater single images. Proc IEEE Int Conf on Computer Vision Workshops, p.825- 830.

[23]Duntley SQ, 1963. Light in the sea. J Opt Soc Am, 53(2): 214-233.

[24]Fabbri C, Islam MJ, Sattar J, 2018. Enhancing underwater imagery using generative adversarial networks. IEEE Int Conf on Robotics and Automation, p.7159-7165.

[25]Finlayson GD, Trezzi E, 2004. Shades of gray and colour constancy. Proc 12th Color Imaging Conf, p.37-41.

[26]Fu XY, Zhuang PX, Huang Y, et al., 2014. A retinex-based enhancing approach for single underwater image. IEEE Int Conf on Image Processing, p.4572-4576.

[27]Fu XY, Liao YH, Zeng DL, et al., 2015. A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans Image Process, 24(12):4965-4977.

[28]Galdran A, Pardo D, Picón A, et al., 2015. Automatic red- channel underwater image restoration. J Vis Commun Image Represent, 26:132-145.

[29]Gao SB, Zhang M, Zhao Q, et al., 2019. Underwater image enhancement using adaptive retinal mechanisms. IEEE Trans Image Process, 28(11):5580-5595.

[30]Ghani ASA, Isa NAM, 2014. Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching. SpringerPlus, 3(1):757.

[31]Ghani ASA, Isa NAM, 2015a. Underwater image quality enhancement through integrated color model with Rayleigh distribution. Appl Soft Comput, 27:219-230.

[32]Ghani ASA, Isa NAM, 2015b. Enhancement of low quality underwater image through integrated global and local contrast correction. Appl Soft Comput, 37:332-344.

[33]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.

[34]Ghani ASA, Aris RSNAR, Zain MLM, 2016. Unsupervised contrast correction for underwater image quality enhancement through integrated-intensity stretched- Rayleigh histograms. J Telecomm Electron Comput Eng, 8(3):1-7.

[35]Giakos GC, 2004. Active backscattered optical polarimetric imaging of scattered targets. Proc 21st IEEE Instrumentation and Measurement Technology Conf, p.430-432.

[36]Gijsenij A, Gevers T, van de Weijer J, 2012. Improving color constancy by photometric edge weighting. IEEE Trans Patt Anal Mach Intell, 34(5):918-929.

[37]Guo YC, Li HY, Zhuang PX, 2020. Underwater image enhancement using a multiscale dense generative adversarial network. IEEE J Ocean Eng, 45(3):862-870.

[38]Han M, Lyu ZY, Qiu T, et al., 2020. A review on intelligence dehazing and color restoration for underwater images. IEEE Trans Syst Man Cybern Syst, 50(5):‏1820-1832.

[39]Han PL, Liu F, Zhang G, et al., 2018. Multi-scale analysis method of underwater polarization imaging. Acta Phys Sin, 67(5):054202 (in Chinese).

[40]Hautière N, Tarel JP, Aubert D, et al., 2008. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol, 27(2):87-95.

[41]He KM, Sun J, Tang XO, 2009. Single image haze removal using dark channel prior. Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.1956-1963.

[42]He N, Wang JB, Zhang LL, et al., 2015. An improved fractional-order differentiation model for image denoising. Signal Process, 112:180-188.

[43]Hou GJ, Pan ZK, Wang GD, et al., 2019. An efficient nonlocal variational method with application to underwater image restoration. Neurocomputing, 369:106-121.

[44]Hu HF, Zhao L, Huang BJ, et al., 2017. Enhancing visibility of polarimetric underwater image by transmittance correction. IEEE Photon J, 9(3):6802310.

[45]Hu HF, Zhao L, Li XB, et al., 2018. Underwater image recovery under the nonuniform optical field based on polarimetric imaging. IEEE Photon J, 10(1):6900309.

[46]Huang BJ, Liu TG, Hu HF, et al., 2016. Underwater image recovery considering polarization effects of objects. Opt Expr, 24(9):9826-9838.

[47]Huang DM, Wang Y, Song W, et al., 2018. Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition. Proc 24th Int Conf on Multimedia Modeling, p.453-465.

[48]Hummel R, 1977. Image enhancement by histogram transformation. Comput Graph Image Process, 6(2):184-195.

[49]Iqbal K, Salam RA, Osman A, et al., 2007. Underwater image enhancement using an integrated colour model. IAENG Int J Comput Sci, 34:2.

[50]Iqbal K, Odetayo M, James A, et al., 2010. Enhancing the low quality images using unsupervised colour correction method. IEEE Int Conf on Systems, Man and Cybernetics, p.1703-1709.

[51]Jaffe JS, 1990. Computer modeling and the design of optimal underwater imaging systems. IEEE J Ocean Eng, 15(2):101-111.

[52]Joshi KR, Kamathe RS, 2008. Quantification of retinex in enhancement of weather degraded images. Int Conf on Audio, Language and Image Processing, p.1229-1233.

[53]Kapoor R, Gupta R, Son LH, et al., 2019. Fog removal in images using improved dark channel prior and contrast limited adaptive histogram equalization. Multim Tools Appl, 78(16):23281-23307.

[54]Kim T, Cha M, Kim H, et al., 2017. Learning to discover cross-domain relations with generative adversarial networks. Proc 34th Int Conf on Machine Learning, p.1857-1865.

[55]Kim TK, Paik JK, Kang BS, 1998. Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans Consum Electron, 44(1):82-87.

[56]Kim YT, 1997. Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron, 43(1):1-8.

[57]Land EH, 1977. The retinex theory of color vision. Sci Am, 237(6):108-128.

[58]Lee Y, Gibson KB, Lee Z, et al., 2014. Stereo image defogging. Proc IEEE Int Conf on Image Processing, p.5427-5431.

[59]Li CY, Guo JC, Cong RM, et al., 2016. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process, 25(12):5664-5677.

[60]Li CY, Guo JC, Guo CL, et al., 2017. A hybrid method for underwater image correction. Patt Recogn Lett, 94:62-67.

[61]Li CY, Guo JC, Guo CL, 2018. Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal Process Lett, 25(3):323- 327.

[62]Li CY, Guo CL, Ren WQ, et al., 2019. An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process, 29:4376-4389.

[63]Li CY, Anwar S, Porikli F, 2020. Underwater scene prior inspired deep underwater image and video enhancement. Patt Recogn, 98:107038.

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

[65]Li YJ, Lu HM, Li KC, et al., 2018. Non-uniform de-scattering and de-blurring of underwater images. Mob Netw Appl, 23(2):352-362.

[66]Liu F, Wei Y, Han PL, et al., 2019. Polarization-based exploration for clear underwater vision in natural illumination. Opt Expr, 27(3):3629-3641.

[67]Liu P, Wang GY, Qi H, et al., 2019. Underwater image enhancement with a deep residual framework. IEEE Access, 7:94614-94629.

[68]Liu TG, Guan ZJ, Li XB, et al., 2020. Polarimetric underwater image recovery for color image with crosstalk compensation. Opt Lasers Eng, 124:105833.

[69]Mangeruga M, Cozza M, Bruno F, 2018. Evaluation of underwater image enhancement algorithms under different environmental conditions. J Mar Sci Eng, 6(1):10.

[70]Marques TP, Albu AB, Hoeberechts M, 2019. A contrast- guided approach for the enhancement of low-lighting underwater images. J Imag, 5(10):79.

[71]McGlamery BL, 1980. A computer model for underwater camera systems. Proc SPIE, Ocean Optics VI, 0208:221-231.

[72]Nomura K, Sugimura D, Hamamoto T, 2018. Underwater image color correction using exposure-bracketing imaging. IEEE Signal Process Lett, 25(6):893-897.

[73]Pan PW, Yuan F, Cheng E, 2018. Underwater image de- scattering and enhancing using DehazeNet and HWD. J Mar Sci Technol, 26(4):531-540.

[74]Pan PW, Yuan F, Cheng E, 2019. De-scattering and edge- enhancement algorithms for underwater image restoration. Front Inform Technol Electron Eng, 20(6):862-871.

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

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

[77]Peng YT, Zhao XY, Cosman PC, 2015. Single underwater image enhancement using depth estimation based on blurriness. IEEE Int Conf on Image Processing, p.4952-4956.

[78]Peng YT, Cao KM, Cosman PC, 2018. Generalization of the dark channel prior for single image restoration. IEEE Trans Image Process, 27(6):2856-2868.

[79]Perez J, Attanasio AC, Nechyporenko N, et al., 2017. A deep learning approach for underwater image enhancement. Int Work-Conf on the Interplay Between Natural and Artificial Computation, p.183-192.

[80]Pizer SM, Amburn EP, Austin JD, et al., 1987. Adaptive histogram equalization and its variations. Comput Vis Graph Image Process, 39(3):355-368.

[81]Raihan AJ, Abas PE, de Silva LC, 2019. Review of underwater image restoration algorithms. IET Image Process, 13(10):1587-1596.

[82]Ren WQ, Liu SF, Ma L, et al., 2019. Low-light image enhancement via a deep hybrid network. IEEE Trans Image Process, 28(9):4364-4375.

[83]Ren WQ, Pan JS, Zhang H, et al., 2020. Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int J Comput Vis, 128(1):240-259.

[84]Reza AM, 2004. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J VLSI Signal Process Syst Signal Image Video Technol, 38(1):35-44.

[85]Roser M, Dunbabin M, Geiger A, 2014. Simultaneous underwater visibility assessment, enhancement and improved stereo. IEEE Int Conf on Robotics and Automation, p.3840-3847.

[86]Schechner YY, Averbuch Y, 2007. Regularized image recovery in scattering media. IEEE Trans Patt Anal Mach Intell, 29(9):1655-1660.

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

[88]Schechner YY, Narasimhan SG, Nayar SK, 2001. Instant dehazing of images using polarization. Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.325-332.

[89]Schechner YY, Narasimhan SG, Nayar SK, 2003. Polarization- based vision through haze. Appl Opt, 42(3):511-525.

[90]Singh D, Kumar V, 2019. A comprehensive review of computational dehazing techniques. Arch Comput Methods Eng, 26(5):1395-1413.

[91]Song W, Wang Y, Huang DM, et al., 2018. A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. Proc 19th Pacific-Rim Conf on Multimedia on Advances in Multimedia Information Processing, p.678-688.

[92]Tang C, von Lukas UF, Vahl M, et al., 2019. Efficient underwater image and video enhancement based on retinex. Signal Image Video Process, 13(5):1011-1018.

[93]Tang JR, Isa NAM, 2017. Bi-histogram equalization using modified histogram bins. Appl Soft Comput, 55:31-43.

[94]Tian Y, Liu B, Su XY, et al., 2019. Underwater imaging based on LF and polarization. IEEE Photon J, 11(1):6500309.

[95]Torres-Méndez LA, Dudek G. 2005. Color correction of underwater images for aquatic robot inspection. Proc 5th Int Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, p.60-73.

[96]Treibitz T, Schechner YY, 2006. Instant 3Descatter. Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.1861-1868.

[97]Treibitz T, Schechner YY, 2009. Active polarization descattering. IEEE Trans Patt Anal Mach Intell, 31(3):385-399.

[98]Treibitz T, Schechner YY, 2012. Turbid scene enhancement using multi-directional illumination fusion. IEEE Trans Image Process, 21(11):4662-4667.

[99]van de Weijer J, Gevers T, Gijsenij A, 2007. Edge-based color constancy. IEEE Trans Image Process, 16(9):2207-2214.

[100]Wang KY, Hu Y, Chen J, et al., 2019. Underwater image restoration based on a parallel convolutional neural network. Remote Sens, 11(13):1591.

[101]Wang SQ, Ma KD, Yeganeh H, et al., 2015. A patch-structure representation method for quality assessment of contrast changed images. IEEE Signal Process Lett, 22(12):2387-2390.

[102]Wang Y, Zhang J, Cao Y, et al., 2017. A deep CNN method for underwater image enhancement. IEEE Int Conf on Image Processing, p.1382-1386.

[103]Wang Y, Song W, Fortino G, et al., 2019. An experimental- based review of image enhancement and image restoration methods for underwater imaging. IEEE Access, 7:140233-140251.

[104]Wang YF, Wang HY, Yin CL, et al., 2016. Biologically inspired image enhancement based on retinex. Neurocomputing, 177:373-384.

[105]Wang Z, Bovik AC, 2006. Modern Image Quality Assessment: Synthesis Lectures on Image, Video, and Multimedia Processing. Morgan & Claypool, San Rafael, Argentina, p.1-156.

[106]Wen HC, Tian YH, Huang TJ, et al., 2013. Single underwater image enhancement with a new optical model. IEEE Int Symp on Circuits and Systems, p.753-756.

[107]Weng CC, Chen H, Fuh CS, 2005. A novel automatic white balance method for digital still cameras. IEEE Int Symp on Circuits and Systems, p.3801-3804.

[108]Wu HD, Zhao M, Li FQ, et al., 2020. Underwater polarization- based single pixel imaging. J Soc Inform Display, 28(2):157-163.

[109]Xie K, Pan W, Xu S, 2018. An underwater image enhancement algorithm for environment recognition and robot navigation. Robotics, 7(1):14.

[110]Xu Q, Guo ZY, Tao QQ, et al., 2015. Transmitting characteristics of polarization information under seawater. Appl Opt, 54(21):6584-6588.

[111]Yang HY, Chen PY, Huang CC, et al., 2011. Low complexity underwater image enhancement based on dark channel prior. 2nd Int Conf on Innovations in Bio-inspired Computing and Applications, p.17-20.

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

[113]Yang M, Hu JT, Li CY, et al., 2019. An in-depth survey of underwater image enhancement and restoration. IEEE Access, 7:123638-123657.

[114]Yemelyanov KM, Lin SS, Pugh EN, et al., 2006. Adaptive algorithms for two-channel polarization sensing under various polarization statistics with nonuniform distributions. Appl Opt, 45(22):5504-5520.

[115]Yin GJ, Liu B, Sheng L, et al., 2019. Semantics disentangling for text-to-image generation. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.2327-2336.

[116]You HF, Tian SW, Yu L, et al., 2020. Pixel-level remote sensing image recognition based on bidirectional word vectors. IEEE Trans Geosci Remote Sens, 58(2):1281-1293.

[117]Yuan MK, Peng YX, 2018. Text-to-image synthesis via symmetrical distillation networks. Proc 26th ACM Int Conf on Multimedia, p.1407-1415.

[118]Yuan MK, Peng YX, 2020. Bridge-GAN: interpretable representation learning for text-to-image synthesis. IEEE Trans Circ Syst Video Technol, 30(11):4258-4268.

[119]Zhang S, Wang T, Dong JY, et al., 2017. Underwater image enhancement via extended multi-scale retinex. Neurocomputing, 245:1-9.

[120]Zhang WD, Dong LL, Pan XP, et al., 2019a. Single image defogging based on multi-channel convolutional MSRCR. IEEE Access, 7:72492-72504.

[121]Zhang WD, Dong LL, Pan XP, et al., 2019b. A survey of restoration and enhancement for underwater images. IEEE Access, 7:182259-182279.

[122]Zhao MH, Hu CQ, Wei FL, et al., 2019. Real-time underwater image recognition with FPGA embedded system for convolutional neural network. Sensors, 19(2):350.

[123]Zhao XW, Jin T, Qu S, 2015. Deriving inherent optical properties from background color and underwater image enhancement. Ocean Eng, 94:163-172.

[124]Zhou JC, Hao ML, Zhang DH, et al., 2019a. Fusion PSPnet image segmentation based method for multi-focus image fusion. IEEE Photon J, 11(6):6501412.

[125]Zhou JC, Zhang DG, Zou PY, et al., 2019b. Retinex-based laplacian pyramid method for image defogging. IEEE Access, 7:122459-122472.

[126]Zhu JY, Park T, Isola P, et al., 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proc IEEE Int Conf on Computer Vision, p.2223-2232.

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