Estimation of ambient light and transmission map with common convolutional architecture

Young Sik Shin; Younggun Cho; Gaurav Pandey; Ayoung Kim

doi:10.1109/OCEANS.2016.7761342

Estimation of ambient light and transmission map with common convolutional architecture

Young Sik Shin, Younggun Cho, Gaurav Pandey, Ayoung Kim

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

77 Scopus citations

Abstract

This paper presents a method for effective ambient light and transmission estimation in underwater images using a common convolutional network architecture. The estimated ambient light and the transmission map are used to dehaze the underwater images. Dehazing underwater images is especially challenging due to the unknown and significantly varying ambient light in underwater environments. Unlike common dehazing methods, the proposed method is capable of estimating ambient light along with the transmission map thereby improving the reconstruction quality of the dehazed images. We evaluate the dehazing performance of the proposed method on real underwater images and also compare our method to current state-of-the-art techniques.

Original language	English
Title of host publication	OCEANS 2016 MTS/IEEE Monterey, OCE 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509015375
DOIs	https://doi.org/10.1109/OCEANS.2016.7761342
State	Published - 28 Nov 2016
Externally published	Yes
Event	2016 OCEANS MTS/IEEE Monterey, OCE 2016 - Monterey, United States Duration: 19 Sep 2016 → 23 Sep 2016

Publication series

Name	OCEANS 2016 MTS/IEEE Monterey, OCE 2016

Conference

Conference	2016 OCEANS MTS/IEEE Monterey, OCE 2016
Country/Territory	United States
City	Monterey
Period	19/09/16 → 23/09/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/OCEANS.2016.7761342

Cite this

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title = "Estimation of ambient light and transmission map with common convolutional architecture",

abstract = "This paper presents a method for effective ambient light and transmission estimation in underwater images using a common convolutional network architecture. The estimated ambient light and the transmission map are used to dehaze the underwater images. Dehazing underwater images is especially challenging due to the unknown and significantly varying ambient light in underwater environments. Unlike common dehazing methods, the proposed method is capable of estimating ambient light along with the transmission map thereby improving the reconstruction quality of the dehazed images. We evaluate the dehazing performance of the proposed method on real underwater images and also compare our method to current state-of-the-art techniques.",

author = "Shin, {Young Sik} and Younggun Cho and Gaurav Pandey and Ayoung Kim",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 OCEANS MTS/IEEE Monterey, OCE 2016 ; Conference date: 19-09-2016 Through 23-09-2016",

year = "2016",

month = nov,

day = "28",

doi = "10.1109/OCEANS.2016.7761342",

language = "English",

series = "OCEANS 2016 MTS/IEEE Monterey, OCE 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "OCEANS 2016 MTS/IEEE Monterey, OCE 2016",

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}

Shin, YS, Cho, Y, Pandey, G & Kim, A 2016, Estimation of ambient light and transmission map with common convolutional architecture. in OCEANS 2016 MTS/IEEE Monterey, OCE 2016., 7761342, OCEANS 2016 MTS/IEEE Monterey, OCE 2016, Institute of Electrical and Electronics Engineers Inc., 2016 OCEANS MTS/IEEE Monterey, OCE 2016, Monterey, United States, 19/09/16. https://doi.org/10.1109/OCEANS.2016.7761342

Estimation of ambient light and transmission map with common convolutional architecture. / Shin, Young Sik; Cho, Younggun; Pandey, Gaurav et al.
OCEANS 2016 MTS/IEEE Monterey, OCE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7761342 (OCEANS 2016 MTS/IEEE Monterey, OCE 2016).

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

TY - GEN

T1 - Estimation of ambient light and transmission map with common convolutional architecture

AU - Shin, Young Sik

AU - Cho, Younggun

AU - Pandey, Gaurav

AU - Kim, Ayoung

PY - 2016/11/28

Y1 - 2016/11/28

N2 - This paper presents a method for effective ambient light and transmission estimation in underwater images using a common convolutional network architecture. The estimated ambient light and the transmission map are used to dehaze the underwater images. Dehazing underwater images is especially challenging due to the unknown and significantly varying ambient light in underwater environments. Unlike common dehazing methods, the proposed method is capable of estimating ambient light along with the transmission map thereby improving the reconstruction quality of the dehazed images. We evaluate the dehazing performance of the proposed method on real underwater images and also compare our method to current state-of-the-art techniques.

AB - This paper presents a method for effective ambient light and transmission estimation in underwater images using a common convolutional network architecture. The estimated ambient light and the transmission map are used to dehaze the underwater images. Dehazing underwater images is especially challenging due to the unknown and significantly varying ambient light in underwater environments. Unlike common dehazing methods, the proposed method is capable of estimating ambient light along with the transmission map thereby improving the reconstruction quality of the dehazed images. We evaluate the dehazing performance of the proposed method on real underwater images and also compare our method to current state-of-the-art techniques.

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DO - 10.1109/OCEANS.2016.7761342

M3 - Conference contribution

AN - SCOPUS:85006958155

T3 - OCEANS 2016 MTS/IEEE Monterey, OCE 2016

BT - OCEANS 2016 MTS/IEEE Monterey, OCE 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 OCEANS MTS/IEEE Monterey, OCE 2016

Y2 - 19 September 2016 through 23 September 2016

ER -

Estimation of ambient light and transmission map with common convolutional architecture

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