In this paper we give new insights into quantitative microwave tomography with robust Huber regularizer and Gauss-Newton optimization. Firstly, we validate this approach for the first time on real electromagnetic measurements. Secondly, we extend the …
In this paper, we present a first-order primal-dual algorithm for tackling the joint demosaicking and deconvolution problem. The proposed algorithm exploits the sparsity of both discrete gradient (TV) and shearlet coefficients as prior knowledge. In …
This work explores the potentials of structure encoding in sparse tomographic reconstructions. We are encoding spatial structure with Markov Random Field (MRF) models and employ it within Magnetic Resonance Imaging (MRI) and Quantitative Microwave …