Near-Field SAR Image Restoration Framework Via Deep Learning

The near-field SAR technology has shown great application value in many fields, such as security inspection, and radar cross section (RCS) measurement. However, due to side-lobe crosstalk and near-field spherical wave effect, the near-field SAR image has high clutter and side-lobe, which leads to serious image degradation. Complex image degradation results in the loss of target structure and contour, which limits the further application of near-field SAR technology. Due to the complex degradation, current restoration methods are not effective enough in terms of weak scattering center and target shape (geometry and structure) restoration. In this article, we first analyze near-field SAR image degradation. Then, utilizing the recent promising deep learning, we propose a novel near-field SAR image restoration framework. In this framework, we construct model-driven 2D CNN for 2D image restoration and 3D CNN for 3D image restoration, respectively. To validate the proposed framework, we construct experiments on simulated 2D and 3D test set, respectively. The experimental results prove the effectiveness of the proposed framework for both 2D and 3D situations.