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Color correction methods for underwater image enhancement: A systematic literature review
Yong Lin Lai1, Tan Fong Ang1, Uzair Aslam Bhatti2
1Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Wilayar Persekutuan, Malaysia.
Plos One
|March 10, 2025
Summary
This review analyzes 13 underwater image enhancement methods, categorized into physical, non-physical, and deep learning approaches. Future research should focus on adaptability and reduced complexity for real-time applications.
Area of Science:
- Computer Vision
- Image Processing
- Marine Technology
Background:
- Underwater images suffer color deviations due to light attenuation, hindering applications like marine surveying and autonomous navigation.
- Effective underwater image enhancement is crucial for improving visibility and data quality in various subsea operations.
Purpose of the Study:
- To systematically review and analyze recent advancements in underwater image color correction methods.
- To identify strengths, limitations, and research gaps in existing underwater image enhancement techniques.
Main Methods:
- A comprehensive literature search across eight databases identified 67 relevant studies (2010-2024).
- Categorization of 13 distinct enhancement methods into physical models, non-physical models, and deep learning-based approaches.
- Critical analysis of algorithmic approaches, data dependency, computational complexity, and performance variability.
Main Results:
- Physical models simulate light attenuation; non-physical models manipulate pixel values; deep learning methods learn mappings from data.
- Persistent challenges include algorithmic limitations, data dependency, computational cost, and variable performance in different underwater environments.
- A taxonomy of methods was established, highlighting critical research gaps.
Conclusions:
- There is a need for improved adaptability of enhancement methods across diverse underwater conditions.
- Reducing computational complexity is essential for enabling real-time underwater image processing applications.
- Further research is required to overcome current challenges and advance the field of underwater image enhancement.
