Sparse regularization method combining SVA for feature enhancement of SAR images

Abstract

Sparse signal processing has been widely used in synthetic aperture radar imaging and feature enhancement of images in the recent decade. Sparse regularization 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. Data availability statement:The data that support the findings of this study are available from the corresponding author upon reasonable request.

HPHR-SAR-Net: Hyperpixel High-Resolution SAR Imaging Network Based on Nonlocal Total Variation

“…In [10], the authors employ regularization techniques to incorporate prior information about interesting features into the imaging formation. Subsequently, numerous studies have enhanced SAR imaging results with convincing outcomes by adding various regularization constraints to highlight different target characteristics [11]- [15].…”