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Spatially Adaptive Kernel Regression Using Risk Estimation

Krishnan, Sunder Ram and Seelamantula, Chandra Sekhar and Chakravarti, Purvasha (2014) Spatially Adaptive Kernel Regression Using Risk Estimation. In: IEEE SIGNAL PROCESSING LETTERS, 21 (4). pp. 445-448.

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Official URL: http://dx.doi.org/10.1109/LSP.2014.2305176


An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to the IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Keywords: Denoising; nonparametric regression; spatially adaptive kernel regression; Stein's unbiased risk estimator (SURE)
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 07 Apr 2014 11:32
Last Modified: 07 Apr 2014 11:32
URI: http://eprints.iisc.ac.in/id/eprint/48816

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