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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10305551 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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