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Mathematical Problems in Engineering
Volume 2010, Article ID 350849, 9 pages
Research Article

Blind Deconvolution for Jump-Preserving Curve Estimation

1Department of Information Science, Faculty of Science, Xi'an Jiaotong University, Shaan Xi 710049, China
2School of Statistics, University of Minnesota, MN 55455, USA

Received 11 February 2010; Accepted 19 February 2010

Academic Editor: Ming Li

Copyright © 2010 Xingfang Huang and Peihua Qiu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.