An adaptive regularized method for deconvolution of signals with edges by convex projections

A new adaptive deconvolution method based on the projection operators onto convex sets (POCS) is presented. A minimum norm least-squares (MNLS) is obtained for signals with edges by means of an estimation-detection-protection scheme. The regularized differentiation technique is necessary for a reaso...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 42; no. 7; pp. 1849 - 1851
Main Author Sanchez-Avila, C.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.07.1994
Institute of Electrical and Electronics Engineers
Subjects
Applied sciences
Deconvolution
Digital signal processing
Exact sciences and technology
Information, signal and communications theory
Iterative algorithms
Iterative methods
Miscellaneous
Seismology
Signal detection
Signal processing
Signal processing algorithms
Singular value decomposition
Speech coding
Telecommunications and information theory
Wiener filter
Deconvolution
Least squares method
Signal processing
Digital processing
Edge detection
Adaptive method
Signal detection
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Summary:A new adaptive deconvolution method based on the projection operators onto convex sets (POCS) is presented. A minimum norm least-squares (MNLS) is obtained for signals with edges by means of an estimation-detection-protection scheme. The regularized differentiation technique is necessary for a reasonable detection of the signal edges. The improvement introduced with this method is illustrated through a simulation example. Finally, a discussion of the wide series of possibilities open along these lines closes this article.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1053-587X
1941-0476
DOI:10.1109/78.298296