Inverse Filtering With Signal-Adaptive Constraints

摘要

This paper discusses linear inverse filtering (deconvolution) from a stochastic signal processing point of view. A direct link between regularized deconvolution and minimum variance estimation is established, and exploited to propose a simple, one-pass optimization procedure. Experimental verification confirms the good results obtainable by the proposed approach.