Scene‐Driven Semantic Alignment for Generative Communication in Underwater Images

Underwater images serve as one of the most intuitive media for human perception of underwater environments. However, the limited bandwidth and susceptibility to noise in underwater acoustic (UWA) channels pose significant challenges for traditional image encoding and transmission methods, thereby hindering high‐quality image reconstruction. Semantic communication aims to shift from pixel‐level to semantic‐level transmission, enhancing both reliability and efficiency. This paper proposes a scene‐guided generative communication method for underwater images with semantic alignment. We decouple and transmit only the essential layout information of underwater scenes. This enables highly efficient compression. At the receiver, we employ a graph convolutional network (GCN) to correct layout distortions and a context‐aware diffusion model to generate realistic underwater images that preserve high semantic fidelity to the original. Experimental results demonstrate that, compared to pixel‐fidelity communication and other generative communication approaches, our method consistently achieves superior image reconstruction quality even under adverse channel conditions, such as extremely low signal‐to‐noise ratios (SNRs), and exhibits significant advantages in downstream tasks.