Compressed Sensing SAR Imaging Based on Centralized Sparse Representation

Sparse representation based synthetic aperture radar (SAR) imaging approaches have shown their superior performance and great potential in compressed sensing SAR imaging field. However, for many existing approaches, the reconstruction accuracy may be affected by inexact observation and low radar sampling ratio. Inspired by sparse representation works in image restoration, we proposed a novel centralized sparse representation based SAR imaging approach. We assume that the imaging result is the degraded (noisy or inaccurate reconstructed) version of the true scattered field. So, the aim of reconstruction is to make the sparse coding coefficients of imaging result as close as possible to the coefficients of the true scattered field. Under this assumption, a centralized sparsity constraint is added into the reconstruction framework to shrink the difference between the coefficients of imaging result and true scattered field and hence make the sparse coding more accurate. To solve the joint optimization problem between updating sparse coding vectors and SAR imaging, a joint optimization method Kusing L-q (0 < q <= 1) regularization is proposed. Experimental results have proven to demonstrate the validity of the proposed approach.