Efficient Semantic Communication for Underwater Images Guided by Physical Priors

Due to the limited bandwidth and severe noise interference in underwater acoustic channels, underwater image transmission typically requires a coding scheme with a high compression ratio and strong robustness. However, existing semantic communication methods are predominantly designed for terrestrial wireless scenarios and do not fully consider the unique characteristics of underwater images and channel features. To address these problems, we propose an efficient semantic communication scheme of underwater images guided by physical priors. Specifically, we design a semantic encoder-decoder with a hybrid architecture of CNN and Swin Transformer for efficient semantic feature extraction and reconstruction. Meanwhile, we design an underwater physical prior fusion module, which leverages physical prior knowledge from underwater imaging to optimize the encoding process, preserving and enhancing essential semantic features. Additionally, a noise-aware training strategy is proposed to guide the network in learning the noise characteristics of measured underwater acoustic channels, improving its robustness in intricate marine conditions. Extensive experiments verify that our method outperforms existing schemes regarding transmission efficiency and image quality.