Premium
Deconvolution with bounded uncertainty
Author(s) -
Combettes Patrick L.,
Trussell H. Joel
Publication year - 1995
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480090103
Subject(s) - deconvolution , a priori and a posteriori , bounded function , signal (programming language) , blind deconvolution , computer science , algorithm , noise (video) , set (abstract data type) , mathematical optimization , noisy data , mathematics , artificial intelligence , image (mathematics) , mathematical analysis , philosophy , epistemology , programming language
In deconvolution problems there are two primary sources of uncertainty in the data formation mechanism, namely measurement noise and errors in the model of the system. In this paper we develop an abstract set theoretic deconvolution framework for problems in which the only information available about these sources of uncertainty consists of bounds. Iterative methods based on projections are used to generate solutions consistent with these bounds, the output data signal and a priori knowledge about the input signal. an example of application of this general framework to discrete signal recovery is demonstrated.