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Sparse Stochastic Processes in Inverse Problems by uunnss is a document available to read on EtoBox.

This document presents a new statistical approach to solving ill-conditioned linear inverse problems based on modeling signals as solutions to sparse stochastic processes. It introduces continuous-domain stochastic models that define signals independently of the reconstruction task. This allows derivation of maximum a posteriori estimators for non-quadratic regularization schemes. The approach characterizes the class of admissible priors, which are confined to Gaussian or sparse distributions. A general rec

Author

uunnss

Language

EN