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. 2025 Mar 10;20(3):e0317306.
doi: 10.1371/journal.pone.0317306. eCollection 2025.

Color correction methods for underwater image enhancement: A systematic literature review

Yong Lin Lai  1 , Tan Fong Ang  1 , Uzair Aslam Bhatti  2 , Chin Soon Ku  3 , Qi Han  4 , Lip Yee Por  1
Affiliations

Color correction methods for underwater image enhancement: A systematic literature review

Yong Lin Lai et al. PLoS One. .

Abstract

Underwater vision is essential in numerous applications, such as marine resource surveying, autonomous navigation, objective detection, and target monitoring. However, raw underwater images often suffer from significant color deviations due to light attenuation, presenting challenges for practical use. This systematic literature review examines the latest advancements in color correction methods for underwater image enhancement. The core objectives of the review are to identify and critically analyze existing approaches, highlighting their strengths, limitations, and areas for future research. A comprehensive search across eight scholarly databases resulted in the identification of 67 relevant studies published between 2010 and 2024. These studies introduce 13 distinct methods for enhancing underwater images, which can be categorized into three groups: physical models, non-physical models, and deep learning-based methods. Physical model-based methods aim to reverse the effects of underwater image degradation by simulating the physical processes of light attenuation and scattering. In contrast, non-physical model-based methods focus on manipulating pixel values without modeling these underlying degradation processes. Deep learning-based methods, by leveraging data-driven approaches, aim to learn mappings between degraded and enhanced images through large datasets. However, challenges persist across all categories, including algorithmic limitations, data dependency, computational complexity, and performance variability across diverse underwater environments. This review consolidates the current knowledge, providing a taxonomy of methods while identifying critical research gaps. It emphasizes the need to improve adaptability across diverse underwater conditions and reduce computational complexity for real-time applications. The review findings serve as a guide for future research to overcome these challenges and advance the field of underwater image enhancement.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. PRISMA flowchart for the study selection process.
Fig 2
Fig 2. Distribution of selected studies by publication year.
Fig 3
Fig 3. Distribution of selected studies by database.
Fig 4
Fig 4. Taxonomy of underwater image color correction methods and challenges.
See this image and copyright information in PMC

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