Inversion of large-support ill-posed linear operators using a piecewise Gaussian MRF
We propose a method for the reconstruction of signals and images observed partially through a linear operator,vith a large support (e.g., a Fourier transform on a sparse set), This inverse problem is ill-posed and we resolve it by incorporating the prior information that the reconstructed objects are composed of smooth regions separated by sharp transitions, This feature is modeled by a piecewise Gaussian (PG) Markov random field (MRF), known also as the weak-string in one dimension and the weak-membrane in two dimensions. The reconstruction is defined as the maximum a posteriori estimate, The prerequisite for the use of such a prior is the success of the optimization stage, The posterior energy corresponding to a PG MRF is generally multimodal and its minimization is particularly problematic. In this context, general forms of simulated annealing rapidly become intractable when the observation operator extends over a large support. In this paper, global optimization is dealt with by extending the graduated nonconvexity (GNC) algorithm to ill-posed linear inverse problems, GNC has been Pioneered by Blake and Zisserman in the field of image segmentation. The resulting algorithm is mathematically suboptimal but it is seen to be very efficient in practice. We show that the original GNC does not correctly apply to ill-posed problems, Our extension is based on a proper theoretical analysis, which provides further insight into the GNC, The performance of the proposed algorithm is corroborated by a synthetic example in the area of diffraction tomography.
tomography discontinuity recovery gnc optimization ill-posed inverse problems image reconstruction map estimation mrf modeling