Sparse reconstruction based reweighted non-convex optimization using homotopy-DCD algorithm

This paper proposed a simple, efficient and easy to hardware implementation iteratively reweighted algorithm to improve the recovery performance for sparse reconstruction from compressive sensing (CS). The algorithm focusses on solving the weighted non-convex penalty minimization problem using homotopy and dichotomous coordinate descent (DCD) algorithm compared with the traditional l1 norm and weighted l1 norm optimization. The numerical experiential results demonstrate that the new proposed method outperforms in signal reconstruction quality.