Total variation regularization-based compressed sensing synthetic aperture radar imaging

Abstract

A total variation (TV) regularization-based compressed sensing (CS) synthetic aperture radar (SAR) imaging algorithm is proposed. Using chirp scaling algorithm as the approximation observation operator and taking TV minimization as the magnitude sparse constraint, an optimization model is designed by separating the magnitude from complex-valued scene. The reconstruction of the SAR image is formulated as a double-parameter joint optimization problem, which is solved by the second-order convex approximation and block coordinate descent approach. Compared with matching filtering method, TV-based image denoising method, and approximation observation-based CS method, the proposed algorithm can reconstruct both sparse and nonsparse scenes with higher accuracy using downsampling raw data. The experimental results via real data validate the effectiveness of the proposed method.