A Nonconvex Approach with Structural Priors for Restoring Underwater Images
Underwater image restoration is a crucial task in various computer vision applications, including underwater target detection and recognition, autonomous underwater vehicles, underwater rescue, marine organism monitoring, and marine geological survey. Among other categories, the physics-based method...
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MDPI AG
2024-11-01
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| author | Hafiz Shakeel Ahmad Awan Muhammad Tariq Mahmood |
| author_facet | Hafiz Shakeel Ahmad Awan Muhammad Tariq Mahmood |
| author_sort | Hafiz Shakeel Ahmad Awan |
| collection | DOAJ |
| description | Underwater image restoration is a crucial task in various computer vision applications, including underwater target detection and recognition, autonomous underwater vehicles, underwater rescue, marine organism monitoring, and marine geological survey. Among other categories, the physics-based methods restore underwater images by improving the transmission map through optimization or regularization techniques. Conventional optimization-based methods often do not consider the effect of structural differences between guidance and transmission maps. To address this issue, in this paper, we present a regularization-based method for restoring underwater images that uses coherent structures between the guidance map and the transmission map. The proposed approach models the optimization of transmission maps through a nonconvex energy function comprising data and smoothness terms. The smoothness term includes static and dynamic structural priors, and the optimization problem is solved using a majorize-minimize algorithm. We evaluate the proposed method on benchmark datasets, and the results demonstrate the superiority of the proposed method over state-of-the-art techniques in terms of improving transmission maps and producing high-quality restored images. |
| format | Article |
| id | doaj-art-db7958327d134c069b10691f55679dbc |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-db7958327d134c069b10691f55679dbc2024-11-26T18:11:48ZengMDPI AGMathematics2227-73902024-11-011222355310.3390/math12223553A Nonconvex Approach with Structural Priors for Restoring Underwater ImagesHafiz Shakeel Ahmad Awan0Muhammad Tariq Mahmood1Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of KoreaFuture Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of KoreaUnderwater image restoration is a crucial task in various computer vision applications, including underwater target detection and recognition, autonomous underwater vehicles, underwater rescue, marine organism monitoring, and marine geological survey. Among other categories, the physics-based methods restore underwater images by improving the transmission map through optimization or regularization techniques. Conventional optimization-based methods often do not consider the effect of structural differences between guidance and transmission maps. To address this issue, in this paper, we present a regularization-based method for restoring underwater images that uses coherent structures between the guidance map and the transmission map. The proposed approach models the optimization of transmission maps through a nonconvex energy function comprising data and smoothness terms. The smoothness term includes static and dynamic structural priors, and the optimization problem is solved using a majorize-minimize algorithm. We evaluate the proposed method on benchmark datasets, and the results demonstrate the superiority of the proposed method over state-of-the-art techniques in terms of improving transmission maps and producing high-quality restored images.https://www.mdpi.com/2227-7390/12/22/3553underwater image restorationimage dehazingrobust regularizationnonconvex optimizationstructural priors |
| spellingShingle | Hafiz Shakeel Ahmad Awan Muhammad Tariq Mahmood A Nonconvex Approach with Structural Priors for Restoring Underwater Images Mathematics underwater image restoration image dehazing robust regularization nonconvex optimization structural priors |
| title | A Nonconvex Approach with Structural Priors for Restoring Underwater Images |
| title_full | A Nonconvex Approach with Structural Priors for Restoring Underwater Images |
| title_fullStr | A Nonconvex Approach with Structural Priors for Restoring Underwater Images |
| title_full_unstemmed | A Nonconvex Approach with Structural Priors for Restoring Underwater Images |
| title_short | A Nonconvex Approach with Structural Priors for Restoring Underwater Images |
| title_sort | nonconvex approach with structural priors for restoring underwater images |
| topic | underwater image restoration image dehazing robust regularization nonconvex optimization structural priors |
| url | https://www.mdpi.com/2227-7390/12/22/3553 |
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