Implementation and Evaluation of Lightweight Deep Learning Models for Real-Time Underwater Image Enhancement: A Comprehensive Review

Abstract
Underwater optical imaging faces significant challenges due to wavelength-dependent light absorption, scattering, and color distortion, which degrade image quality and hinder marine exploration applications. Traditional enhancement methods often lack adaptability to diverse underwater conditions, while conventional deep learning approaches impose prohibitive computational demands for real-time deployment on resource-constrained platforms. This comprehensive review systematically examines the state-of-the-art in lightweight deep learning architectures specifically designed for real-time underwater image enhancement. We present a detailed taxonomy of efficiency-oriented designs including depth wise separable convolutions, attention mechanisms, neural architecture search, and model compression techniques. The paper critically analyzes implementation strategies, benchmark datasets, and evaluation metrics, both perceptual quality indicators and computational efficiency measures. Furthermore, we synthesize comparative performance analyses across multiple lightweight architectures and identify persistent challenges in domain generalization, temporal consistency, and hardware-software co-design. Emerging research directions including physics-informed networks, multimodal fusion, and ultra-low-power deployment paradigms are discussed. This review aims to consolidate current knowledge and guide future research toward robust, efficient vision systems for underwater autonomous platforms.
Keywords: Underwater Image Enhancement; Lightweight Deep Learning; Real-Time Processing; Model Compression; Autonomous Underwater Vehicles (AUVs) ; Computational Efficiency; Edge Computing; Edge AI

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