In recent years, a compressive sensing based underwater imaging system has been under investigation: the Compressive Line Sensing (CLS) imaging system. In the CLS system, each line segment is sensed independently; with regard to signal reconstruction, the correlation among the adjacent lines is exploited via the joint sparsity in the distributed compressive sensing model. Interestingly, the dynamic compressive sensing signal model is also capable of exploiting the correlated nature of the adjacent lines through a Bayesian framework. This paper proposes a new CLS reconstruction technique through the integration of these different models, and includes an evaluation of the proposed technique using the experiment dataset obtained from an underwater imaging test setup.