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

Abstract

High resolution is a key trend in the development of synthetic aperture radar (SAR), which enables the capture of fine details and accurate representation of backscattering properties. However, traditional high-resolution SAR imaging algorithms face several challenges. Firstly, these algorithms tend to focus on local information, neglecting nonlocal information between different pixel patches. Secondly, speckle is more pronounced and difficult to filter out in high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging generally involves high time and computational complexity, making real-time imaging difficult to achieve. To address these issues, we propose a hyperpixel high-resolution SAR imaging network (HPHR-SAR-Net) for rapid despeckling in high-resolution modes. Based on the concept of hyper-pixel techniques, we initially combine nonconvex and nonlocal total variation as compound regularization. This approach more effectively despeckles and manages the relationship between pixels while reducing bias effects caused by convex constraints. Subsequently, we solve the compound regularization model using the Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into a Deep Unfolded Network (DUN). The network's parameters are adaptively learned in a data-driven manner, and the learned network significantly increases imaging speed. Additionally, the proposed HPHR-SAR-Net is compatible with high-resolution imaging modes such as spotlight, staring spotlight, and sliding spotlight. In this paper, we demonstrate the superiority of HPHR-SAR-Net through experiments in both simulated and real SAR scenarios. The results indicate that HPHR-SAR-Net can rapidly perform highresolution SAR imaging from raw echo data, producing accurate imaging results.Index Terms-Synthetic aperture radar (SAR), sparse microwave imaging, hyper-pixel, high-resolution, deep unfolding network (DUN), alternating direction method of multipliers (ADMM) I. INTRODUCTIONS YNTHETIC aperture radar (SAR) is an active imaging system. Unlike optical sensors, SAR transmits microwave signals with surface penetration capabilities, enabling it to

Pseudo-L0-Norm Fast Iterative Shrinkage Algorithm Network: Agile Synthetic Aperture Radar Imaging via Deep Unfolding Network

“…Although optimization-based CS methods have achieved better recovered performance, these methods still suffer from some problems, i.e., manual tuning and pre-definition of optimization parameters (e.g., the regularization parameter and the thresholding parameter), high computational complexity, low signalto-noise ratio (SNR) resistance, and the prior assumption of observed scenes. With the development of deep learning techniques, some network-based CS methods are proposed to address the manual tuning issue and improve the performance and speed of signal reconstruction [20][21][22][23][24][25][26][27][28][29]. Specifically, deep unfolding networks relate optimization-based CS methods with deep neural networks so that they can provide better interpretability, and these methods have been successfully applied in SAR imaging for remote sensing.…”