  In this paper, we present a compressive sampling and Multi-Hypothesis (MH)
reconstruction strategy for video sequences which has a rather simple encoder,
while the decoding system is not that complex. We introduce a convex cost
function that incorporates the MH technique with the sparsity constraint and
the Tikhonov regularization. Consequently, we derive a new iterative algorithm
based on these criteria. This algorithm surpasses its counterparts (Elasticnet
and Tikhonov) in the recovery performance. Besides it is computationally much
faster than the Elasticnet and comparable to the Tikhonov. Our extensive
simulation results confirm these claims.
