Nonconvex-Nonlocal Total Variation Regularization-Based Joint Feature-Enhanced Sparse SAR Imaging

Synthetic aperture radar (SAR) imaging under the sparse constraint is a developing SAR imaging scheme that emerged in the recent decade.
$\ell _{1}$
-norm and the total variation (TV)-norm are two widely used constraints in the reconstruction model for enhancing the features of the point distributed targets, respectively. However,
$\ell _{1}$
regularization often generates a biased estimation, and the TV regularization always over-smooths isolated point targets in the scene. Thus, we propose the nonconvex-nonlocal TV (NLTV) regularization in this letter, which can: 1) improve the reconstruction accuracy and 2) avoid over-smoothing of the isolated point targets while simultaneously enhancing the features of the point and distributed targets. The modified variable splitting-alternating direction method of multipliers (VS-ADMMs) is introduced to solve the compound regularization. The performance of the proposed method is verified using simulated data and real data.