A GIBBS MARKOV RANDOM-FIELD MODEL FOR ACTIVE IMAGING AT MICROWAVE-FREQUENCIES

Reconstruction of object surfaces from sparse measures is an ill-posed inverse problem which requires a priori knowledge to be regularized. This problem becomes more difficult whenever an active method is used and a scattering medium is present between the signal source and the s cene observed. This paper describes a reconstruction method that can b e applied to solve a microwave imaging problems. The goal of the propo sed algorithm is to obtain a pixel-based representation of 2-D object slices. The objects are assumed to be inhomogeneous dielectric scatter ers in a microwave electromagnetic field. The method is based on the h ypothesis that the observed field is a Markov Random Field (MRF), and consists in finding the field configuration that maximizes the a poste riori probability measure associated with the MRF model. A specific pr obabilistic measure that is based on a weak-membrane regularizing cons traint as an a priori model and on an observation model using a near-f ield hypothesis is proposed. A classical stochastic optimization appro ach (i.e., simulated annealing with a Metropolis sampler) is adopted t o find the probabilistic maximum. The capabilities and effectiveness o f the method are evaluated and compared with those of other approaches requiring matrix inversion. Finally, simulation results are reported that show better reconstructions, than those obtained by other microwa ve image domain methods.