A Nonconvex Approach with Structural Priors for Restoring Underwater Images

Hafiz Shakeel Ahmad Awan and Muhammad Tariq Mahmood ()
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Hafiz Shakeel Ahmad Awan: Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of Korea
Muhammad Tariq Mahmood: Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of Korea

Mathematics, 2024, vol. 12, issue 22, 1-20

Abstract: 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.

Keywords: underwater image restoration; image dehazing; robust regularization; nonconvex optimization; structural priors (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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