  We consider the total variation (TV) minimization problem used for
compressive sensing and solve it using the generalized alternating projection
(GAP) algorithm. Extensive results demonstrate the high performance of proposed
algorithm on compressive sensing, including two dimensional images,
hyperspectral images and videos. We further derive the Alternating Direction
Method of Multipliers (ADMM) framework with TV minimization for video and
hyperspectral image compressive sensing under the CACTI and CASSI framework,
respectively. Connections between GAP and ADMM are also provided.
