A Multiplicative Regularizer Augmented With Spatial Priors for Microwave Imaging

Abstract

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial (incomplete) SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from microwave imaging. Index Terms-Microwave imaging (MWI), regularization, spatial priors (SP), inversion.

Microwave Imaging of the Neck by Means of Artificial Neural Networks for Tumor Detection

“…However, this fact does not prevent the identification of the tumor. Moreover, the spinal cord is properly recognized inside the vertebral bone, too, both in the relative dielectric permittivity and electric conductivity (e.g., estimated values of 25-35 vs. the actual values of [30][31][32][33][34][35][36][37][38][39][40]. It is worth noting that in general, the dielectric properties of the muscle and tumor tissues may overlap.…”