Dong Liang1, Leslie Ying2
1Department of Electrical Engineering and
Computer Science , University of Wisconsin-Milwaukee, Milwaukee, WI, United
States; 2Department of Electrical Engineering and Computer
Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
Both
L1 and homotopic L0 minimizations have been used in compressed-sensing MRI
reconstruction. In this abstract, we propose a homotopic L0-L1 hybrid
minimization algorithm such that it has the benefit of both L1 and homotopic
L0 minimizations. The proposed algorithm minimizes the L0 quasi-norm of large
transform coefficients but the L1 norm of small transform coefficients for
the image to be reconstructed. The simulation results show the proposed
algorithm outperforms both L1 and homotopic L0 minimization algorithms when
the same reduction factor is used.