Sparse regularization method combining SVA for feature enhancement of SAR images

Sparse signal processing has been widely used in synthetic aperture radar imaging and feature enhancement of images in the recent decade. Sparse regularization l(1) can reduce the imaging noise level and suppress sidelobes. However, the suppression of sidelobes by sparse regularization often pays the price of losing information of weak targets. Therefore, the sparse regularization method combining spatially variant apodization is proposed in this paper, which can suppress noise, sidelobes and retain detail information. The performance of the proposed method is verified using simulated and real data.