Imaging Inverse Problems and Sparse Stochastic Modeling
Seminar • 12 December 2011 • BM 4.233AbstractThis talk considers deriving a family of MAP estimators, based on the theory of continuous-domain sparse stochastic processes introduced by Unser et al., for inverse problems occur in imaging. The family includes potential functions that are typically nonconvex in addition to the traditional methods of Tikhonov and total-variation (TV) regularization. We also derive an algorithmic scheme for handling nonconvex problems. Further, we compare the reconstruction performance of different estimators for the problem of MR image reconstruction.