Abstract: Underwater image quality is seriously degraded as a result of light scattering and absorption, which poses challenges of color distortion, reduced visibility, haze, and noise. Such visual degradation poses significant challenges to faithful object identification and hampers important applications such as marine exploration, underwater surveillance, and autonomous vehicle navigation. Traditional image-enhancement approaches are inefficient in restoring image fidelity. In order to tackle these problems, we introduce a sophisticated underwater image enhancement system that combines deep learning-based object detection with dedicated processing blocks for color correction, haze removal, and noise reduction. By using this combined approach, natural color tones are restored, scattering effects are minimized, and noise is reduced, hence improving visual quality and detection robustness. Our solution is aimed at facilitating real-time underwater operations like marine biodiversity analysis, autonomous navigation, and emergency response with enhanced accuracy and decision-making abilities.
Keywords: Underwater Image Processing, Object Detection, Color Correction, Dehazing, Denoising, Marine Research.
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DOI:
10.17148/IJARCCE.2025.14575
