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

Motivated by the application of neural networks in super resolutionmicroscopy, this paper considers super resolution as the mapping oftrigonometric moments of a discrete measure on $[0,1)^d$ to its support andweights. We prove that this map satisfies a local Lipschitz property where wegive explicit estimates for the Lipschitz constant depending on the dimension$d$ and the sampling effort. Moreover, this local Lipschitz estimate allows toconclude that super resolution with the Wasserstein distance as the metric onthe parameter space is even globally Lipschitz continuous. As a byproduct, weimprove an estimate for the smallest singular value of multivariate Vandermondematrices having pairwise clustering nodes.

Sparse super resolution is Lipschitz continuous