Full Text:   <1606>

Summary:  <159>

CLC number: TP391.41

On-line Access: 2019-07-08

Received: 2017-11-10

Revision Accepted: 2018-07-12

Crosschecked: 2019-06-11

Cited: 0

Clicked: 1677

Citations:  Bibtex RefMan EndNote GB/T7714


Fei Yuan


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.6 P.862-871


De-scattering and edge-enhancement algorithms for underwater image restoration

Author(s):  Pan-wang Pan, Fei Yuan, En Cheng

Affiliation(s):  MOE Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Xiamen 361005, China; more

Corresponding email(s):   yuanfei@xmu.edu.cn

Key Words:  Image de-scattering, Edge enhancement, Convolutional neural network, Non-subsampled contourlet transform

Pan-wang Pan, Fei Yuan, En Cheng. De-scattering and edge-enhancement algorithms for underwater image restoration[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(6): 862-871.

@article{title="De-scattering and edge-enhancement algorithms for underwater image restoration",
author="Pan-wang Pan, Fei Yuan, En Cheng",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T De-scattering and edge-enhancement algorithms for underwater image restoration
%A Pan-wang Pan
%A Fei Yuan
%A En Cheng
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 6
%P 862-871
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700744

T1 - De-scattering and edge-enhancement algorithms for underwater image restoration
A1 - Pan-wang Pan
A1 - Fei Yuan
A1 - En Cheng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 6
SP - 862
EP - 871
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700744

Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.




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


[1]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.

[2]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.

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

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

[5]Drews P Jr, do Nascimento E, Moraes F, et al., 2013. Transmission estimation in underwater single images. IEEE Int Conf on Computer Vision Workshops, p.825-830.

[6]Finlayson GD, Trezzi E, 2004. Shades of gray and colour constancy. The 12th Color Imaging Conf: Color Science and Engineering Systems, Technologies, Applications, p.37-41.

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

[8]Goodfellow IJ, Warde-Farley D, Mirza M, et al., 2013. Maxout networks. https://arxiv.org/abs/1302.4389?context=stat

[9]He KM, Sun J, Tang XO, 2011. Single image haze removal using dark channel prior. IEEE Trans Patt Anal Mach Intell, 33(2):2341-2353.

[10]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.

[11]Li Y, Hu J, Jia Y, 2014. Automatic SAR image enhancement based on nonsubsampled contourlet transform and memetic algorithm. Neurocomputing, 134:70-78.

[12]Mao BQ, Jin XM, 2010. Application of self-adaptive histogram equalization algorithm to image enhancement processing. J Hebei North Univ (Nat Sci Ed), 26(5):64-68 (in Chinese).

[13]Padmavathi G, Subashini P, Kumar MM, 2010. Comparison of filters used for underwater image pre-processing. Int J Comput Sci Netw Secur, 10(1):58-65.

[14]Pan JS, Sun DQ, Pfister H, et al., 2016. Blind image deblurring using dark channel prior. IEEE Conf on Computer Vision and Pattern Recognition, p.1628-1636.

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

[16]Schechner YY, Karpel N, 2006. Recovery of underwater visibility and structure by polarization analysis. IEEE J Ocean Eng, 30(3):570-587.

[17]Tan CS, Sluzek A, Seet GLG, et al., 2007. Range gated imaging system for underwater robotic vehicle. OCEANS Asia Pacific, p.1-6.

[18]Tang KT, Yang JC, Wang J, 2014. Investigating haze-relevant features in a learning framework for image dehazing. IEEE Conf on Computer Vision and Pattern Recognition, p.2995-3002.

[19]Tarel JP, Hautiere N, Caraffa L, et al., 2012. Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell Trans Syst Mag, 4(2):6-20.

[20]Thakur VS, Tripathi N, 2010. On the way towards efficient enhancement of multi-channel underwater images. Int J Appl Eng Res, 5(5):895-903.

[21]Tomasi C, Manduchi R, 1998. Bilateral filtering for gray and color images. Proc 6th Int Conf on Computer Vision, p.839-846.

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

[23]Vasamsetti S, Mittal N, Neelapu BC, et al., 2017. Wavelet based perspective on variational enhancement technique for underwater imagery. Ocean Eng, 141:88-100.

[24]Wang HL, Cai WY, Yang JY, et al., 2015. Design of HD video surveillance system for deep-Sea biological exploration. Proc IEEE 16th Int Conf on Communication Technology, p.908-911.

[25]Yang AP, Zhang LY, Qu C, et al., 2017. Underwater images visibility improving algorithm with weighted L1 regularization. J Electron Inform Technol, 39(3):626-633 (in Chinese).

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

[27]Zhang SP, Zeng P, Luo XM, et al., 2012. Multi-scale retinex with color restoration and detail compensation. J Xi’an Jiaotong Univ, 46(4):32-37 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE