Volume 9 • Issue 2 • PP: 15–21 • 2024
Bio-Inspired Image Enhancement Algorithms for Underwater Surveillance
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Underwater surveillance relies heavily on image quality, yet underwater environments present unique challenges, including low visibility, color distortion, and light scattering. This study proposes a bio-inspired image enhancement algorithm designed to address these challenges by mimicking adaptive mechanisms found in marine organisms. The algorithm integrates a multi-scale Retinex model with a bio-inspired filter based on visual properties of aquatic species, optimizing contrast and color balance for improved image clarity. Tested on various underwater image datasets, the proposed method achieved a 45% improvement in contrast enhancement and a 38% reduction in color distortion compared to traditional enhancement techniques. Furthermore, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) improved by 42% and 35%, respectively. The results demonstrate the algorithm’s effectiveness in enhancing visibility and detail, enabling more accurate object detection and classification in underwater surveillance. The bio-inspired approach offers a practical solution for underwater monitoring, particularly valuable for applications in marine research, environmental monitoring, and security.
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