On Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction

Authors:
Z. G. Zhang;S. C. Chan
Affiliations:
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
Venue:
Journal of Signal Processing Systems
Year:
2011

Citing 11
Cited 0

Improving image resolution using subpixel motion

Pattern Recognition Letters
Improving resolution by image registration

CVGIP: Graphical Models and Image Processing
Super-Resolution Imaging

Super-Resolution Imaging
A frequency domain approach to registration of aliased images with application to super-resolution

EURASIP Journal on Applied Signal Processing
On Bandwidth Selection in Local Polynomial Regression Analysis and Its Application to Multi-resolution Analysis of Non-uniform Data

Journal of Signal Processing Systems
A new method for varying adaptive bandwidth selection

IEEE Transactions on Signal Processing
Performance analysis of the adaptive algorithm for bias-to-variance tradeoff

IEEE Transactions on Signal Processing
Fast and robust multiframe super resolution

IEEE Transactions on Image Processing
A spatially adaptive nonparametric regression image deblurring

IEEE Transactions on Image Processing
Multiframe demosaicing and super-resolution of color images

IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper studies the problem of adaptive kernel selection for multivariate local polynomial regression (LPR) and its application to smoothing and reconstruction of noisy images. In multivariate LPR, the multidimensional signals are modeled locally by a polynomial using least-squares (LS) criterion with a kernel controlled by a certain bandwidth matrix. Based on the traditional intersection confidence intervals (ICI) method, a new refined ICI (RICI) adaptive scale selector for symmetric kernel is developed to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of multidimensional signals. The resulting multivariate LPR method called the steering-kernel-based LPR with refined ICI method (SK-LPR-RICI) is applied to the smoothing and reconstruction problems in noisy images. Simulation results show that the proposed SK-LPR-RICI method has a better PSNR and visual performance than conventional LPR-based methods in image processing.