Majorization–Minimization approach for real-time enhancement of sparsity-driven SAR imaging

Abstract

The earlier works in the context of sparsity-driven SAR imaging have shown significant improvement in the reconstruction process due to admitting sparsity as a prior. In spite of the importance of real-time processing requirement in the remote sensing (RS) applications, most of the works have not focused on real-time procedures and reducing the computational burden, but rather enhancing the quality of formed image. To address this weakness, this paper presents a problem-driven algorithm, which relies on Majorization–Minimization (MM) procedure. Using MM in our solutions, a simpler surrogate optimization problem is solved instead of the difficult original form. To show the efficacy of MM algorithm in real-time applications experimental results based on simulated and real data along with a performance analysis are presented. All results validate the superiority of the proposed MM-based method in terms of computational load and processing time as compared with previous works.