Multivariate Vector Subdivision Schemes with a General Matrix-valued Filter
Ran Lu

TL;DR
This paper introduces a unified framework for analyzing the convergence of multivariate vector subdivision schemes with general matrix-valued filters, extending beyond classical Lagrange and Hermite schemes, with theoretical insights and practical examples.
Contribution
It defines a unique meaningful way to characterize vector subdivision schemes with general matrix filters and links their convergence to the vector cascade algorithm.
Findings
Unified convergence analysis for general matrix-valued filters
Extension of existing convergence results for classical schemes
Examples illustrating the theoretical framework
Abstract
Subdivision schemes are closely related to splines and wavelets and have numerous applications in CAGD and numerical differential equations. Subdivision schemes employ a scalar filter; that is, scalar subdivision schemes, have been extensively studied in the literature. In contrast, subdivision schemes with a matrix filter, which are the so-called vector subdivision schemes, are far from being well understood. So far, only vector subdivision schemes that use special matrix-valued filters have been well-investigated, such as the Lagrange and Hermite subdivision schemes. To the best of our knowledge, it remains unclear how to define and characterize the convergence of a vector subdivision scheme that uses a general matrix-valued filter. Though filters from Lagrange and Hermite subdivision schemes have nice properties and are widely used in practice, filters not from either subdivision…
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Taxonomy
TopicsDigital Filter Design and Implementation
