Local Polynomial Approximation Technique in Image Processing
Date Issued
2009
Date
2009
Author(s)
Chen, Pei-Jiun
Abstract
The main idea of this thesis is to discuss local polynomial approximation (LPA) employed in image processing. It is also called local polynomial regression. LPA is used to reconstruct signals.n Chapter 2, the observation model of LPA is introduced, and so is the basis weighting window. How it is applied to signal synthesis and reconstruction is also explained here. The bandwidth of weighting window plays an important role in the accuracy of signal estimation. In order to find the ideal window size, we employ a statistical technique, intersection of confidence intervals (ICI).or relatively complex image signals, using directional windows can reserve more details of the image. The anisotropic LPA-ICI kernel in Chapter 4 is one of the directional windows.he time-frequency transform using LPA is briefly introduced in Chapter 5. pplications of Local Polynomial Approximation technique in image processing are in the last Chapter, including denoising, deblurring, and color filter array (CFA).
Subjects
local
polynomial approximation
regression
image processing
Type
thesis
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