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

Signals and images with discontinuities appear in many problems in suchdiverse areas as biology, medicine, mechanics, and electrical engineering. Theconcrete data are often discrete, indirect and noisy measurements of somequantities describing the signal under consideration. A frequent task is tofind the segments of the signal or image which corresponds to finding thediscontinuities or jumps in the data. Methods based on minimizing the piecewiseconstant Mumford-Shah functional -- whose discretized version is known as Pottsfunctional -- are advantageous in this scenario, in particular, in connectionwith segmentation. However, due to their non-convexity, minimization of suchfunctionals is challenging. In this paper we propose a new iterativeminimization strategy for the multivariate Potts functional dealing withindirect, noisy measurements. We provide a convergence analysis and underpinour findings with numerical experiments.

Iterative Potts minimization for the recovery of signals with discontinuities from indirect measurements -- the multivariate case