An Accurate SAR Imaging Method Based on Total Variation & Nonconvex Regularization

Sparse signal processing is widely used in synthetic aperture radar (SAR) imaging with the development of compressed sensing. As a typical sparse reconstruction method, L-1 regularization generally causes bias effect. In this paper, we linearly combine the nonconvex penalty and the total variation (TV)-norm penalty as a compound regularizer in the imaging model, which can improve the reconstruction accuracy of targets as well as maintain the continuity of the backscattering coefficient in uniform regions. Simulations and experiments based on real data from Gaofen-3 are performed to verify the effectiveness of the proposed method.