Abstract
Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are used in SAR imaging, including L1 and TV penalty, L1 and L2,1 penalty, etc. In this article, a new adaptive multiple regularization parameters selection method named L-hypersurface is proposed. The effectiveness of the proposed method is verified by experiments. Simulation experiments indicate that the selected optimal regularization parameters have satisfied reconstruction results, both visually and numerically. Furthermore, experiments on Gaofen-3 SAR satellite data are also exploited to show the performance of the proposed method.
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