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Regular Paper

Real-Time Underwater Image Enhancement Using Adaptive Full-Scale Retinex

School of Information, Yunnan University of Finance and Economics, Kunming 650221, China
School of Electrical Electronics and Computer Science, Guangxi University of Science and Technology Liuzhou 545006, China
Department of Information and Communication, College of the Chinese People's Armed Police Force Chengdu 610213, China
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Abstract

Current Retinex-based image enhancement methods with fixed scale filters cannot adapt to situations involving various depths of field and illuminations. In this paper, a simple but effective method based on adaptive full-scale Retinex (AFSR) is proposed to clarify underwater images or videos. First, we design an adaptive full-scale filter that is guided by the optical transmission rate to estimate illumination components. Then, to reduce the computational complexity, we develop a quantitative mapping method instead of non-linear log functions for directly calculating the reflection component. The proposed method is capable of real-time processing of underwater videos using temporal coherence and Fourier transformations. Compared with eight state-of-the-art clarification methods, our method yields comparable or better results for image contrast enhancement, color-cast correction and clarity.

Keywords

image enhancementunderwaterRetineximaging through turbulent media

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Journal of Computer Science and Technology
Volume 38 Issue 4,
July 2023
Pages 885-898
DOI: 10.1007/s11390-022-1115-z
Cite this article:
Xu X-G, Fan X-S, Liu Y-L. Real-Time Underwater Image Enhancement Using Adaptive Full-Scale Retinex. Journal of Computer Science and Technology, 2023, 38(4): 885-898. https://doi.org/10.1007/s11390-022-1115-z

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Received: 22 April 2021
Accepted: 22 February 2022
Published: 06 December 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023
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