# Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

## TL;DR

This paper introduces a novel radar imaging method that combines sparse optimization with MRF clustering priors, improving image reconstruction by exploiting clustered sparse structures.

## Contribution

It proposes integrating an MRF prior with FISTA for enhanced sparse signal reconstruction in radar imaging, which is a new approach in this context.

## Key findings

- Outperforms standard FISTA in image quality.
- Demonstrates advantages over existing MRF-based methods.
- Validated through simulations and experiments.

## Abstract

Recent progress in compressive sensing states the importance of exploiting intrinsic structures in sparse signal reconstruction. In this letter, we propose a Markov random field (MRF) prior in conjunction with fast iterative shrinkagethresholding algorithm (FISTA) for image reconstruction. The MRF prior is used to represent the support of sparse signals with clustered nonzero coefficients. The proposed approach is applied to the inverse synthetic aperture radar (ISAR) imaging problem. Simulations and experimental results are provided to demonstrate the performance advantages of this approach in comparison with the standard FISTA and existing MRF-based methods.