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Underwater Optical-Sonar Image Fusion Systems
Unmanned underwater operations using remotely operated vehicles or unmanned surface vehicles are increasing in recent times, and this guarantees human safety and work efficiency. Optical cameras and multi-beam sonars are generally used as imaging sensors in underwater environments. However, the obta...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655726/ https://www.ncbi.nlm.nih.gov/pubmed/36366142 http://dx.doi.org/10.3390/s22218445 |
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author | Kim, Hong-Gi Seo, Jungmin Kim, Soo Mee |
author_facet | Kim, Hong-Gi Seo, Jungmin Kim, Soo Mee |
author_sort | Kim, Hong-Gi |
collection | PubMed |
description | Unmanned underwater operations using remotely operated vehicles or unmanned surface vehicles are increasing in recent times, and this guarantees human safety and work efficiency. Optical cameras and multi-beam sonars are generally used as imaging sensors in underwater environments. However, the obtained underwater images are difficult to understand intuitively, owing to noise and distortion. In this study, we developed an optical and sonar image fusion system that integrates the color and distance information from two different images. The enhanced optical and sonar images were fused using calibrated transformation matrices, and the underwater image quality measure (UIQM) and underwater color image quality evaluation (UCIQE) were used as metrics to evaluate the performance of the proposed system. Compared with the original underwater image, image fusion increased the mean UIQM and UCIQE by 94% and 27%, respectively. The contrast-to-noise ratio was increased six times after applying the median filter and gamma correction. The fused image in sonar image coordinates showed qualitatively good spatial agreement and the average IoU was 75% between the optical and sonar pixels in the fused images. The optical-sonar fusion system will help to visualize and understand well underwater situations with color and distance information for unmanned works. |
format | Online Article Text |
id | pubmed-9655726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96557262022-11-15 Underwater Optical-Sonar Image Fusion Systems Kim, Hong-Gi Seo, Jungmin Kim, Soo Mee Sensors (Basel) Article Unmanned underwater operations using remotely operated vehicles or unmanned surface vehicles are increasing in recent times, and this guarantees human safety and work efficiency. Optical cameras and multi-beam sonars are generally used as imaging sensors in underwater environments. However, the obtained underwater images are difficult to understand intuitively, owing to noise and distortion. In this study, we developed an optical and sonar image fusion system that integrates the color and distance information from two different images. The enhanced optical and sonar images were fused using calibrated transformation matrices, and the underwater image quality measure (UIQM) and underwater color image quality evaluation (UCIQE) were used as metrics to evaluate the performance of the proposed system. Compared with the original underwater image, image fusion increased the mean UIQM and UCIQE by 94% and 27%, respectively. The contrast-to-noise ratio was increased six times after applying the median filter and gamma correction. The fused image in sonar image coordinates showed qualitatively good spatial agreement and the average IoU was 75% between the optical and sonar pixels in the fused images. The optical-sonar fusion system will help to visualize and understand well underwater situations with color and distance information for unmanned works. MDPI 2022-11-03 /pmc/articles/PMC9655726/ /pubmed/36366142 http://dx.doi.org/10.3390/s22218445 Text en © 2022 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 Kim, Hong-Gi Seo, Jungmin Kim, Soo Mee Underwater Optical-Sonar Image Fusion Systems |
title | Underwater Optical-Sonar Image Fusion Systems |
title_full | Underwater Optical-Sonar Image Fusion Systems |
title_fullStr | Underwater Optical-Sonar Image Fusion Systems |
title_full_unstemmed | Underwater Optical-Sonar Image Fusion Systems |
title_short | Underwater Optical-Sonar Image Fusion Systems |
title_sort | underwater optical-sonar image fusion systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655726/ https://www.ncbi.nlm.nih.gov/pubmed/36366142 http://dx.doi.org/10.3390/s22218445 |
work_keys_str_mv | AT kimhonggi underwateropticalsonarimagefusionsystems AT seojungmin underwateropticalsonarimagefusionsystems AT kimsoomee underwateropticalsonarimagefusionsystems |