Enhancing Underwater Imagery Using Generative Adversarial Networks

Cameron Fabbri, Md Jahidul Islam, Junaed Sattar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Autonomous underwater vehicles (AUVs) rely on a variety of sensors - acoustic, inertial and visual - for intelligent decision making. Due to its non-intrusive, passive nature and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. AUVs that rely on visual sensing thus face difficult challenges and consequently exhibit poor performance on vision-driven tasks. This paper proposes a method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline. Furthermore, we show how recently proposed methods are able to generate a dataset for the purpose of such underwater image restoration. For any visually-guided underwater robots, this improvement can result in increased safety and reliability through robust visual perception. To that effect, we present quantitative and qualitative data which demonstrates that images corrected through the proposed approach generate more visually appealing images, and also provide increased accuracy for a diver tracking algorithm.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7159-7165
Number of pages7
ISBN (Electronic)9781538630815
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018
CountryAustralia
CityBrisbane
Period5/21/185/25/18

Fingerprint

Autonomous underwater vehicles
Light refraction
Image reconstruction
Light absorption
Pipelines
Decision making
Acoustics
Robots
Color
Sensors
Water

Cite this

Fabbri, C., Islam, M. J., & Sattar, J. (2018). Enhancing Underwater Imagery Using Generative Adversarial Networks. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018 (pp. 7159-7165). [8460552] (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2018.8460552

Enhancing Underwater Imagery Using Generative Adversarial Networks. / Fabbri, Cameron; Islam, Md Jahidul; Sattar, Junaed.

2018 IEEE International Conference on Robotics and Automation, ICRA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 7159-7165 8460552 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Fabbri, C, Islam, MJ & Sattar, J 2018, Enhancing Underwater Imagery Using Generative Adversarial Networks. in 2018 IEEE International Conference on Robotics and Automation, ICRA 2018., 8460552, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 7159-7165, 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, 5/21/18. https://doi.org/10.1109/ICRA.2018.8460552
Fabbri C, Islam MJ, Sattar J. Enhancing Underwater Imagery Using Generative Adversarial Networks. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 7159-7165. 8460552. (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ICRA.2018.8460552
Fabbri, Cameron ; Islam, Md Jahidul ; Sattar, Junaed. / Enhancing Underwater Imagery Using Generative Adversarial Networks. 2018 IEEE International Conference on Robotics and Automation, ICRA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 7159-7165 (Proceedings - IEEE International Conference on Robotics and Automation).
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