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Enhancement and Optimization of Underwater Images and Videos Mapping

Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate co...

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Detalles Bibliográficos
Autores principales: Li, Chengda, Dong, Xiang, Wang, Yu, Wang, Shuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305551/
https://www.ncbi.nlm.nih.gov/pubmed/37420873
http://dx.doi.org/10.3390/s23125708
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author Li, Chengda
Dong, Xiang
Wang, Yu
Wang, Shuo
author_facet Li, Chengda
Dong, Xiang
Wang, Yu
Wang, Shuo
author_sort Li, Chengda
collection PubMed
description Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.
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spelling pubmed-103055512023-06-29 Enhancement and Optimization of Underwater Images and Videos Mapping Li, Chengda Dong, Xiang Wang, Yu Wang, Shuo Sensors (Basel) Article Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene. MDPI 2023-06-19 /pmc/articles/PMC10305551/ /pubmed/37420873 http://dx.doi.org/10.3390/s23125708 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Chengda
Dong, Xiang
Wang, Yu
Wang, Shuo
Enhancement and Optimization of Underwater Images and Videos Mapping
title Enhancement and Optimization of Underwater Images and Videos Mapping
title_full Enhancement and Optimization of Underwater Images and Videos Mapping
title_fullStr Enhancement and Optimization of Underwater Images and Videos Mapping
title_full_unstemmed Enhancement and Optimization of Underwater Images and Videos Mapping
title_short Enhancement and Optimization of Underwater Images and Videos Mapping
title_sort enhancement and optimization of underwater images and videos mapping
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10305551/
https://www.ncbi.nlm.nih.gov/pubmed/37420873
http://dx.doi.org/10.3390/s23125708
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