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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Underwater Light Field Depth Map Restoration Using Deep Convolutional Neural Fields

verfasst von : Huimin Lu, Yujie Li, Hyoungseop Kim, Seiichi Serikawa

Erschienen in: Artificial Intelligence and Robotics

Verlag: Springer International Publishing

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Abstract

Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. In order to solve these issues, a great deal of work has been used to improve the quality of underwater images. Most of them used the high-intensity LED for lighting to obtain the high contrast images. However, in high turbidity water, high-intensity LED causes strong scattering and absorption. In this paper, we firstly propose a light field imaging approach for solving underwater depth map estimation problems in low-intensity lighting environment. As a solution, we tackle the problem of de-scattering from light field images by using deep convolutional neural fields in depth estimation. Experimental results show the effectiveness of the proposed method through challenging real world underwater imaging.

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Vorheriges Kapitel Motor Anomaly Detection for Aerial Unmanned Vehicles Using Temperature Sensor
Nächstes Kapitel Image Processing Based on the Optimal Threshold for Signature Verification
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Metadaten
Titel
Underwater Light Field Depth Map Restoration Using Deep Convolutional Neural Fields
verfasst von
Huimin Lu
Yujie Li
Hyoungseop Kim
Seiichi Serikawa
Copyright-Jahr
2018
Buch
Artificial Intelligence and Robotics

Print ISBN: 978-3-319-69876-2

Electronic ISBN: 978-3-319-69877-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-69877-9_33