Underwater Image Restoration Based on Red Channel and Haze-Lines Prior

Dabing Yu; Guanying Huo; Yan Liu; Yan Zhou; Jinxing Xu

doi:10.1007/978-3-030-27532-7_13

Underwater Image Restoration Based on Red Channel and Haze-Lines Prior

Dabing Yu
, Guanying Huo^*
, Yan Liu
, Yan Zhou
, Jinxing Xu

^*Corresponding author for this work

Hohai University Changzhou
2Changzhou Key Laboratory of Sensor Networks and Environmental Sensing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Due to the scattering and absorption of light while it propagates in the water, underwater images often suffer from low contrast and color distortion. In order to solve this problem, we propose an underwater image restoration algorithm based on red channel and haze-lines prior in this paper. Firstly, the red channel prior is used to estimate veiling-light. Secondly, according to the characteristics of red channel attenuation in water, the attenuation ratio of red-blue channel and red-green channel are introduced to estimate the transmission by using haze-lines prior. Finally, the transmission is corrected by the red channel boundary constraint. In addition, for underwater artificial illumination, we introduce saturation as the low bound of the transmission estimation to reduce the impact of artificial light. The experimental results show that the proposed algorithm can restore image color information, improve image clarity and obtain better visual quality. The quantitative analysis indicates that the proposed algorithm performs well on a wide variety of underwater images and is competitive with other state-of-the-arts.

Original language	English
Title of host publication	Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
Editors	Haibin Yu, Jinguo Liu, Lianqing Liu, Yuwang Liu, Zhaojie Ju, Dalin Zhou
Publisher	Springer Verlag
Pages	148-158
Number of pages	11
ISBN (Print)	9783030275310
DOIs	https://doi.org/10.1007/978-3-030-27532-7_13
State	Published - 2019
Externally published	Yes
Event	12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 - Shenyang, China Duration: 8 Aug 2019 → 11 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11741 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
Country/Territory	China
City	Shenyang
Period	8/08/19 → 11/08/19

Keywords

Artificial illumination
Haze-lines
Red channel
Saturation
Transmission
Underwater image restoration

Access to Document

10.1007/978-3-030-27532-7_13

Cite this

Yu, D., Huo, G., Liu, Y., Zhou, Y., & Xu, J. (2019). Underwater Image Restoration Based on Red Channel and Haze-Lines Prior. In H. Yu, J. Liu, L. Liu, Y. Liu, Z. Ju, & D. Zhou (Eds.), Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings (pp. 148-158). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11741 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-27532-7_13

Yu, Dabing ; Huo, Guanying ; Liu, Yan et al. / Underwater Image Restoration Based on Red Channel and Haze-Lines Prior. Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. editor / Haibin Yu ; Jinguo Liu ; Lianqing Liu ; Yuwang Liu ; Zhaojie Ju ; Dalin Zhou. Springer Verlag, 2019. pp. 148-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{af98fbcb58b1494caf0eadd76333cfa5,

title = "Underwater Image Restoration Based on Red Channel and Haze-Lines Prior",

abstract = "Due to the scattering and absorption of light while it propagates in the water, underwater images often suffer from low contrast and color distortion. In order to solve this problem, we propose an underwater image restoration algorithm based on red channel and haze-lines prior in this paper. Firstly, the red channel prior is used to estimate veiling-light. Secondly, according to the characteristics of red channel attenuation in water, the attenuation ratio of red-blue channel and red-green channel are introduced to estimate the transmission by using haze-lines prior. Finally, the transmission is corrected by the red channel boundary constraint. In addition, for underwater artificial illumination, we introduce saturation as the low bound of the transmission estimation to reduce the impact of artificial light. The experimental results show that the proposed algorithm can restore image color information, improve image clarity and obtain better visual quality. The quantitative analysis indicates that the proposed algorithm performs well on a wide variety of underwater images and is competitive with other state-of-the-arts.",

keywords = "Artificial illumination, Haze-lines, Red channel, Saturation, Transmission, Underwater image restoration",

author = "Dabing Yu and Guanying Huo and Yan Liu and Yan Zhou and Jinxing Xu",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",

year = "2019",

doi = "10.1007/978-3-030-27532-7\_13",

language = "英语",

isbn = "9783030275310",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "148--158",

editor = "Haibin Yu and Jinguo Liu and Lianqing Liu and Yuwang Liu and Zhaojie Ju and Dalin Zhou",

booktitle = "Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings",

address = "德国",

}

Yu, D, Huo, G, Liu, Y, Zhou, Y & Xu, J 2019, Underwater Image Restoration Based on Red Channel and Haze-Lines Prior. in H Yu, J Liu, L Liu, Y Liu, Z Ju & D Zhou (eds), Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11741 LNAI, Springer Verlag, pp. 148-158, 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019, Shenyang, China, 8/08/19. https://doi.org/10.1007/978-3-030-27532-7_13

Underwater Image Restoration Based on Red Channel and Haze-Lines Prior. / Yu, Dabing; Huo, Guanying; Liu, Yan et al.
Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. ed. / Haibin Yu; Jinguo Liu; Lianqing Liu; Yuwang Liu; Zhaojie Ju; Dalin Zhou. Springer Verlag, 2019. p. 148-158 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11741 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Underwater Image Restoration Based on Red Channel and Haze-Lines Prior

AU - Yu, Dabing

AU - Huo, Guanying

AU - Liu, Yan

AU - Zhou, Yan

AU - Xu, Jinxing

PY - 2019

Y1 - 2019

N2 - Due to the scattering and absorption of light while it propagates in the water, underwater images often suffer from low contrast and color distortion. In order to solve this problem, we propose an underwater image restoration algorithm based on red channel and haze-lines prior in this paper. Firstly, the red channel prior is used to estimate veiling-light. Secondly, according to the characteristics of red channel attenuation in water, the attenuation ratio of red-blue channel and red-green channel are introduced to estimate the transmission by using haze-lines prior. Finally, the transmission is corrected by the red channel boundary constraint. In addition, for underwater artificial illumination, we introduce saturation as the low bound of the transmission estimation to reduce the impact of artificial light. The experimental results show that the proposed algorithm can restore image color information, improve image clarity and obtain better visual quality. The quantitative analysis indicates that the proposed algorithm performs well on a wide variety of underwater images and is competitive with other state-of-the-arts.

AB - Due to the scattering and absorption of light while it propagates in the water, underwater images often suffer from low contrast and color distortion. In order to solve this problem, we propose an underwater image restoration algorithm based on red channel and haze-lines prior in this paper. Firstly, the red channel prior is used to estimate veiling-light. Secondly, according to the characteristics of red channel attenuation in water, the attenuation ratio of red-blue channel and red-green channel are introduced to estimate the transmission by using haze-lines prior. Finally, the transmission is corrected by the red channel boundary constraint. In addition, for underwater artificial illumination, we introduce saturation as the low bound of the transmission estimation to reduce the impact of artificial light. The experimental results show that the proposed algorithm can restore image color information, improve image clarity and obtain better visual quality. The quantitative analysis indicates that the proposed algorithm performs well on a wide variety of underwater images and is competitive with other state-of-the-arts.

KW - Artificial illumination

KW - Haze-lines

KW - Red channel

KW - Saturation

KW - Transmission

KW - Underwater image restoration

UR - https://www.scopus.com/pages/publications/85070607930

U2 - 10.1007/978-3-030-27532-7_13

DO - 10.1007/978-3-030-27532-7_13

M3 - 会议稿件

AN - SCOPUS:85070607930

SN - 9783030275310

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 148

EP - 158

BT - Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings

A2 - Yu, Haibin

A2 - Liu, Jinguo

A2 - Liu, Lianqing

A2 - Liu, Yuwang

A2 - Ju, Zhaojie

A2 - Zhou, Dalin

PB - Springer Verlag

T2 - 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019

Y2 - 8 August 2019 through 11 August 2019

ER -

Yu D, Huo G, Liu Y, Zhou Y, Xu J. Underwater Image Restoration Based on Red Channel and Haze-Lines Prior. In Yu H, Liu J, Liu L, Liu Y, Ju Z, Zhou D, editors, Intelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings. Springer Verlag. 2019. p. 148-158. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-27532-7_13

Underwater Image Restoration Based on Red Channel and Haze-Lines Prior

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