Dynamic recovery for block sparse signals

摘要

As an important extension to sparsity, Block Structured Sparsity (BSS) has been widely investigated and a large set of algorithms have been designed to recover signals with BSS. In this paper, a dynamic system is proposed to recover signals with BSS, namely D-BSS. Particularly, D-BSS turns to exploiting the dynamic systems governed by an ℓ_(2,1) norm constraint. It can be proved that the equilibrium of D-BSS is equivalent to optimum of traditional BSS algorithms, e.g., Group-lasso. Simulation results are given to illustrate the desirable performance of the D-BSS system, especially the improvement of convergence rate.