Near-Field SAR Image Restoration Based On Two Dimensional Spatial-Variant Deconvolution

TL;DR

This paper introduces a 2D spatial-variant deconvolution method for near-field SAR image restoration, effectively handling spatially varying degradation to improve image quality for large objects like aircraft.

Contribution

It proposes a novel 2D spatial-variant deconvolution approach using cyclic coordinate descent, overcoming limitations of existing methods for large-scale object imaging.

Findings

Higher accuracy in target amplitude estimation

Improved position localization of targets

Validated effectiveness on simulation and real data

Abstract

Images of near-field SAR contains spatial-variant sidelobes and clutter, subduing the image quality. Current image restoration methods are only suitable for small observation angle, due to their assumption of 2D spatial-invariant degradation operation. This limits its potential for large-scale objects imaging, like the aircraft. To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed. First, the image degradation is seen as a complex convolution process with 2D spatial-variant operations. Then, to restore the image, the process of deconvolution is performed by cyclic coordinate descent algorithm. Experiments on simulation and measured data validate the effectiveness and superiority of the proposed method. Compared with current methods, higher precision estimation of the targets' amplitude and position is obtained.