A Machine Learning-Driven Framework for Enhancing Underwater Visual Signal Processing in Marine Ecosystem Monitoring and Anthropogenic Impact Assessment

Jian Hu^*

• North China University of Technology, Beijing, China

Recent advancements in underwater monitoring technologies have highlighted the critical need for intelligent systems capable of addressing the unique visual challenges of marine environments. Optical distortions, ecological variability, and dynamic biological behaviors pose significant obstacles to conventional image processing methods, often leading to suboptimal signal interpretations that undermine environmental monitoring and assessments of anthropogenic impacts. Traditional methodologies, primarily adapted from terrestrial computer vision, fail to adequately account for spectral attenuation, scattering effects, and the ecological semantics intrinsic to underwater scenes, thereby limiting their effectiveness in tasks such as marine species tracking, seafloor habitat mapping, and anomaly detection. To overcome these limitations, we introduce a machine learning-based framework that integrates physics-aware visual modeling with ecological adaptivity. This framework comprises the Bio-Optical Attenuation Neural Extractor (BOANE) and the Context-Aware Marine Signal Enhancement (CAMSE) module. BOANE employs spectral-adaptive convolutional units and depth-aware feature modulation to correct radiance distortions and encode biologically relevant visual information. CAMSE enhances this by dynamically adjusting parameters based on real-time ecological priors and optical conditions, incorporating flow-stabilized feature alignment, confidence-aware semantic filtering, and biologically informed regularization. Experimental results demonstrate substantial improvements in signal clarity, temporal consistency, and ecological interpretability on challenging underwater datasets, establishing a robust approach for data-driven underwater visual signal processing. By embedding optical physics and ecological semantics into the computational pipeline, this framework sets a new standard for adaptive, semantically aware analysis of marine imagery, enabling high-fidelity monitoring of marine ecosystems in complex and variable underwater environments.