Incorporating Spatial Priors in Microwave Imaging via Multiplicative Regularization

摘要

This article presents a microwave imaging (MWI) algorithm that can incorporate prior structural information, also known as spatial priors (SP), about the object being imaged to enhance the achievable image quantitative accuracy. This algorithm: 1) is fully automated and 2) can work with both complete and partially available structural information. The core idea of this imaging algorithm is to use a multiplicative regularization term to incorporate SP, and a second regularization term to handle the lack of structural information in a given part of the imaging domain. This algorithm, which has been implemented for the 2-D transverse magnetic case, is evaluated against single-frequency and multiple-frequency synthetic and experimental MWI data sets.