학술논문
Gradient-Adaptive Normalized Convolution
Document Type
Periodical
Author
Source
IEEE Signal Processing Letters IEEE Signal Process. Lett. Signal Processing Letters, IEEE. 15:489-492 2008
Subject
Language
ISSN
1070-9908
1558-2361
1558-2361
Abstract
Signal estimation for sparsely and irregularly sampled signals can be carried out using either noniterative methods, or iterative methods or methods that deal with irregular samples and their uncertainty, through normalized convolution. The latter is a general method for filtering incomplete or uncertain data and is based on the separation of both data and operator into a signal part and a certainty part. It has been proven that normalized convolution yields a local description which is optimal both in an algebraic and a least-squares sense. In this letter, we employ the normalized convolution concept to formulate a novel reconstruction method for irregularly sampled signals, utilizing an anisotropic, rotated applicability filter. Our experimental results demonstrate performance gains in a least-squares sense, retaining edge and contour information, especially in sparsely sampled areas on the image plane.