Fast Spatio-Temporal Signal Recovery Based on Differential Smoothness Regularization

Spatio-temporal signal recovery has been extensively studied, focusing on leveraging signal structures, such as low-rankness and high spatio-temporal smoothness, to recover complete signals. This paper introduces a fast algorithm for spatial-temporal signal recovery based on the differential smoothness regularization, utilizing the alternating direction method of multipliers (ADMM). Our proposed algorithm updates variables through associated closed-form solutions in each ADMM iteration and is guaranteed to converge to the global optimum of the optimization problem. Extensive numerical results are presented, demonstrating the superior performance of the proposed algorithm over state-of-the-art approaches.