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

Bayesian inference methods have been widely applied in inverse problems,{largely due to their ability to characterize the uncertainty associated withthe estimation results.} {In the Bayesian framework} the prior distribution ofthe unknown plays an essential role in the Bayesian inference, {and a goodprior distribution can significantly improve the inference results.} In thispaper, we extend the total~variation-Gaussian (TG) prior in \cite{Z.Yao2016},and propose a hybrid prior distribution which combines the nonlocal totalvariation regularization and the Gaussian (NLTG) distribution. The advantage ofthe new prior is two-fold. The proposed prior models both texture and geometricstructures present in images through the NLTV. The Gaussian reference measurealso provides a flexibility of incorporating structure information from areference image. Some theoretical properties are established for the NLTGprior. The proposed prior is applied to limited-angle tomography reconstructionproblem with difficulties of severe data missing. We compute both MAP and CMestimates through two efficient methods and the numerical experiments validatethe advantages and feasibility of the proposed NLTG prior.

NLTG Priors in Medical Image: Nonlocal TV-Gaussian (NLTG) prior for Bayesian inverse problems with applications to Limited CT Reconstruction