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Spatially adaptive kernel regression using risk estimation

Publication Type : Journal Article

Publisher : IEEE Signal Processing Letters

Source : IEEE Signal Processing Letters, vol. 21, no. 4, pp. 445–448, April 2014

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Campus : Amritapuri

School : School of Computing

Department : Computer Science and Engineering

Year : 2014

Abstract : 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.

Cite this Research Publication : S. R. Krishnan, C. S. Seelamantula, and P. Chakravarti, “Spatially adaptive kernel regression using risk estimation,” IEEE Signal Processing Letters, vol. 21, no. 4, pp. 445–448, April 2014

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