Deblurring subject to nonnegativity constraints

Csiszar's I-divergence is used as a discrepancy measure for deblurring subject to the constraint that all functions involved are nonnegative. An iterative algorithm is proposed for minimizing this measure. It is shown that every function in the sequence is nonnegative and the sequence converges...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 40; no. 5; pp. 1143 - 1150
Main Authors Snyder, D.L., Schulz, T.J., O'Sullivan, J.A.
Format Journal Article
LanguageEnglish
Published IEEE 01.05.1992
Subjects
Deconvolution
Integral equations
Iterative algorithms
Iterative methods
Kernel
Laboratories
Stochastic resonance
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ISSN1053-587X
DOI10.1109/78.134477

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Summary:Csiszar's I-divergence is used as a discrepancy measure for deblurring subject to the constraint that all functions involved are nonnegative. An iterative algorithm is proposed for minimizing this measure. It is shown that every function in the sequence is nonnegative and the sequence converges monotonically to a global minimum. Other properties of the algorithm are shown, including lower bounds on the improvement in the I-divergence at each step of the algorithm and on the difference between the I-difference at step k and at the limit point. A method for regularizing the solution is proposed.< >
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ISSN:1053-587X
DOI:10.1109/78.134477