Multimodal deep learning for intelligent camera parameter control in underwater optical camera communication imaging

Le résumé fourni par la source

Underwater optical camera communication (UOCC) relies on image-based signal reception, where the quality of recorded stripes directly determines the achievable optical signal-to-noise ratio (SNR). However, fixed camera parameters—such as exposure level and International Standards Organization (ISO) sensitivity—are often inadequate under dynamically varying aquatic conditions including turbidity, flow velocity, and ambient illumination. To overcome these limitations, we propose a multimodal deep model that fuses visual cues with environmental context to predict scene-optimal camera parameters at capture time. A ResNet50 backbone extracts semantic representations from raw stripe images, while environmental factors—including turbidity, flow speed, ambient illumination, and LED power—are jointly encoded through a parallel embedding architecture. These modalities are fused within a regression network to infer settings that maximize imaging clarity and stability. The experimental results demonstrate that the proposed model achieves robust accuracy. Crucially, the capture-time parameter selection improves stripe visibility and delivers an average ∼3dB gain in optical SNR across diverse conditions. This capture-time optimization sustains a higher and more stable SNR band than both the original fixed settings and a representative learning-based post-processing baseline (DnCNN with horizontal-banding suppression). Beyond accuracy, the approach is computationally light and portable: context-aware parameter prediction at capture time eliminates per-frame processing, providing a practical route to real-time, resource-constrained UOCC with enhanced image quality and robustness.

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