Abstract: Underwater images (UWIs) captured by underwater detectors are essential for underwater detection and exploration. The compressive sensing theory (CS) provides a method for recovering images from few measurements, and it has been proven to be suitable for underwater environments with narrow bandwidth and limited communication channel resources, which may have a significant negative impact on the quality of captured UWIs. However, most existing state-of-art CS methods do not take the characteristics of UWIs into account, so their performance is limited in underwater applications. Compared with on-land images, UWIs have the following characteristics: 1) UWIs contain relatively few semantics, with a large amount of similar feature within the same semantics; 2) The importance of different semantics in UWIs is closely related to the underwater imaging model. In this paper, we combine the underwater imaging model and semantic of UWIs with CS task and propose a novel semantic maps-guided CS framework for UWIs, dubbed SEM-UCSNet, which can improve the performance of sampling and reconstruction, especially under extremely low sampling rate. In the sampling stage, a semantic importance analysis module combined with the imaging model is designed to guide the sampling. In the reconstruction process, a graph-based reconstruction strategy guided by semantic maps is proposed to model all features under the same semantic and mine complementarity between them to improve the reconstruction quality. Simultaneously, we introduce GAN into the underwater CS reconstruction task and use sampled features as conditions to make the reconstructed UWIs have richer details. Experimental results on some real-world UWIs datasets have demonstrated the superiority of our SEM-UCSNet on both objective and subjective metrics.