English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Comparing with univariate framelets, the main challenge involved in studyingmultivariate framelets is that we have to deal with the highly non-trivialproblem of factorizing multivariate polynomial matrices. As a consequence,multivariate framelets are much less studied than univariate framelets in theliterature. Among existing works on multivariate framelets, multivariatemultiframelets are much less considered comparing with the exitensively studiedscalar framelets. Hence multiframelets are far from being well understood. Inthis paper, we focus on multivariate dual multiframelets (or dual vectorframelets) obtained through the popular oblique extension principle (OEP),which are called OEP-based dual multiframelets. We will show that from anygiven pair of compactly supported refinable vector functions, one can alwaysconstruct an OEP-based dual mltiframelet, such that its generators have thehighest possible order of vanishing moments. Moreover, the associated discreteframelet transform is compact and sparse.

Compactly supported multivariate dual multiframelets with high vanishing moments and high balancing orders