From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques
The underwater world has gained significant attention in research in recent years, particularly in the context of ocean exploration. Images serve as a valuable data source for underwater tasks, but they face several issues related to light behavior in this environment. Given the complexity of captur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/5/161 |
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| author | Lucas Amparo Barbosa Antonio Lopes Apolinario |
| author_facet | Lucas Amparo Barbosa Antonio Lopes Apolinario |
| author_sort | Lucas Amparo Barbosa |
| collection | DOAJ |
| description | The underwater world has gained significant attention in research in recent years, particularly in the context of ocean exploration. Images serve as a valuable data source for underwater tasks, but they face several issues related to light behavior in this environment. Given the complexity of capturing data from the sea and the large variability of environmental components (depth, distance, suspended particles, turbidity, etc.), synthesized underwater scenes can provide relevant data to improve image processing algorithms and computer vision tasks. The main goal of this survey is to summarize techniques to underwater image synthesis, their contributions and correlations, and to highlight further directions and opportunities in this research domain. |
| format | Article |
| id | doaj-art-e8f840ceef2e450ba8db3973c420ce3e |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-e8f840ceef2e450ba8db3973c420ce3e2025-08-20T01:56:19ZengMDPI AGJournal of Imaging2313-433X2025-05-0111516110.3390/jimaging11050161From Physically Based to Generative Models: A Survey on Underwater Image Synthesis TechniquesLucas Amparo Barbosa0Antonio Lopes Apolinario1Institute of Computing, Federal University of Bahia, Salvador 40170-110, BrazilInstitute of Computing, Federal University of Bahia, Salvador 40170-110, BrazilThe underwater world has gained significant attention in research in recent years, particularly in the context of ocean exploration. Images serve as a valuable data source for underwater tasks, but they face several issues related to light behavior in this environment. Given the complexity of capturing data from the sea and the large variability of environmental components (depth, distance, suspended particles, turbidity, etc.), synthesized underwater scenes can provide relevant data to improve image processing algorithms and computer vision tasks. The main goal of this survey is to summarize techniques to underwater image synthesis, their contributions and correlations, and to highlight further directions and opportunities in this research domain.https://www.mdpi.com/2313-433X/11/5/161underwater imagerycomputer visionphysically based renderinggenerative models |
| spellingShingle | Lucas Amparo Barbosa Antonio Lopes Apolinario From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques Journal of Imaging underwater imagery computer vision physically based rendering generative models |
| title | From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques |
| title_full | From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques |
| title_fullStr | From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques |
| title_full_unstemmed | From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques |
| title_short | From Physically Based to Generative Models: A Survey on Underwater Image Synthesis Techniques |
| title_sort | from physically based to generative models a survey on underwater image synthesis techniques |
| topic | underwater imagery computer vision physically based rendering generative models |
| url | https://www.mdpi.com/2313-433X/11/5/161 |
| work_keys_str_mv | AT lucasamparobarbosa fromphysicallybasedtogenerativemodelsasurveyonunderwaterimagesynthesistechniques AT antoniolopesapolinario fromphysicallybasedtogenerativemodelsasurveyonunderwaterimagesynthesistechniques |