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Blind Deconvolution for Jump-Preserving Curve Estimation
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-05-09
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
In many applications, observed signals are contaminated by both random noise and blur.
This paper proposes a blind deconvolution procedure for estimating a regression function with possible jumps preserved, by removing both noise and blur when recovering the signals.
Our procedure is based on three local linear kernel estimates of the regression function, constructed from observations in a left-side, a right-side, and a two-side neighborhood of a given point, respectively.
The estimated function at the given point is then defined by one of the three estimates with the smallest weighted residual sum of squares.
To better remove the noise and blur, this estimate can also be updated iteratively.
Performance of this procedure is investigated by both simulation and real data examples, from which it can be seen that our procedure performs well in various cases.
American Psychological Association (APA)
Huang, Xingfang& Qiu, Peihua. 2010. Blind Deconvolution for Jump-Preserving Curve Estimation. Mathematical Problems in Engineering،Vol. 2010, no. 2010, pp.1-9.
https://search.emarefa.net/detail/BIM-464953
Modern Language Association (MLA)
Huang, Xingfang& Qiu, Peihua. Blind Deconvolution for Jump-Preserving Curve Estimation. Mathematical Problems in Engineering No. 2010 (2010), pp.1-9.
https://search.emarefa.net/detail/BIM-464953
American Medical Association (AMA)
Huang, Xingfang& Qiu, Peihua. Blind Deconvolution for Jump-Preserving Curve Estimation. Mathematical Problems in Engineering. 2010. Vol. 2010, no. 2010, pp.1-9.
https://search.emarefa.net/detail/BIM-464953
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-464953