Blind Deconvolution for Jump-Preserving Curve Estimation

Blind Deconvolution for Jump-Preserving Curve Estimation

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...

A Jump-Preserving Curve Fitting Procedure Based On Local Piecewise-Linear Kernel Estimation

It is known that the fitted regression function based on conventional local smoothing procedures is not statistically consistent at jump positions of the true regression function. In this article, a curve-fitting procedure based on local piecewise-linear kernel estimation is suggested. In a neighborhood of a given point, a piecewise-linear function with a possible jump at the given point is fit...

Wavelet Estimation via Sparse Multichannel Blind Deconvolution

Guided Filter based Edge-preserving Image Non-blind Deconvolution

In this work, we propose a new approach for efficient edgepreserving image deconvolution. Our algorithm is based on a novel type of explicit image filter guided filter. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. We propose an efficient iterative algorithm with the decouple of deblurring and denoi...

Blind Deconvolution of Vehicle Inductive Signatures for Travel Time Estimation

Travel-time data provides vital information for traffic monitoring, management, and planning. The objective of this research was to develop a new computational approach that could accurately measure travel time from two sets of spatially separated loop detectors using re-identification of vehicle inductance signatures generated by the loops. Although measuring travel time using loop inductance ...