Quasi-tight Framelets with Directionality or High Vanishing Moments Derived from Arbitrary Refinable Functions
Chenzhe Diao, Bin Han

TL;DR
This paper introduces a constructive method to derive directional quasi-tight framelets with high vanishing moments from arbitrary refinable functions, enhancing multivariate wavelet theory and applications.
Contribution
The paper presents algorithms to construct directional quasi-tight framelets with high vanishing moments from any given refinable function, including conditions for tightness and directionality.
Findings
Constructive algorithms for quasi-tight framelets from arbitrary refinable functions.
Ability to derive directional tight framelets with nonnegative low-pass filters.
Framelets with high vanishing moments and simple high-pass filter structure.
Abstract
Construction of multivariate tight framelets is known to be a challenging problem. Multivariate dual framelets with vanishing moments generalize tight framelets and are not easy to be constructed either. Compactly supported multivariate framelets with directionality or high vanishing moments are of interest and importance in both theory and applications. In this paper we introduce the notion of a quasi-tight framelet, which is a dual framelet, but behaves almost like a tight framelet. Let be an arbitrary compactly supported -refinable function such that its underlying low-pass filter satisfies the basic sum rule. We first constructively prove by a step-by-step algorithm that we can always easily derive from the arbitrary -refinable function a directional compactly supported quasi-tight -framelet in associated with a directional quasi-tight…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMathematical Analysis and Transform Methods · Seismic Imaging and Inversion Techniques · Image and Signal Denoising Methods
