A Multiplicative Regularizer Augmented With Spatial Priors for Microwave Imaging

The standard weighted L-2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging (MWI) algorithms is modified to take into account structural prior information, also known as spatial priors (SPs), 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 MWI algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR 1) can handle partial (incomplete) SP and 2) can, to some extent, enhance the quantitative accuracy achievable from MWI.

Automated summary

The research modified the standard weighted L-2 norm total variation multiplicative regularization (MR) originally developed for microwave imaging (MWI) algorithms to incorporate structural prior information about the object being imaged, known as spatial priors (SPs). The proposed augmented MR (AMR) approach requires minimal changes to existing MWI algorithms while being able to handle partial spatial priors and enhance quantitative accuracy achievable from MWI to some extent, as demonstrated with two experimental data sets.