An adaptive multi-threshold iterative shrinkage algorithm for microwave imaging applications

Microwave Imaging represents a potential, interesting modality for a great variety of applications in different fields, not only for its capability to detect unknown targets in a non-destructive fashion, but also for the quantitative characterization of such scatterers. In medical imaging applications, the illposedness and non-linearity of the electromagnetic (EM) inverse problem still represents a big challenge before moving to clinical trials. This paper applies a novel thresholding algorithm to the linear inversion at each iteration of the Distorted Born Iterative Method (DBIM), which is amongst the most popular algorithms that deal with the EM nonlinear problem. This approach is computationally tractable and represents a good trade-off between low computational burden and image accuracy.

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