Optimum healthcare image smoothing and restoration based on the two-dimensional ARMA model

Terence O'Neill, Jack H W Penm, Jonathan Penm

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Abstract

A two-dimensional autoregressive - moving average (ARMA) model has been recently developed by Penm (1999) which leads to optimum recursive enhancement procedures for realistic image data. This paper considers the application of these electronic healthcare informatics procedures to data whose spatial covariance function appears to vary exponentially with Euclidean distance. Specifically, the identification problem is considered, an optimal recursive algorithm based on a two-dimensional ARMA model is developed for a specific example, and this algorithm is compared with the ad-hoc method of successive orthogonalisation approximations.
Original languageEnglish
Title of host publicationCollaborative research in electronic healthcare
Subtitle of host publicationBioinformatics, pharmacy informatics and computing
EditorsT.J. O'Neill, J. Penm, R.D. Terrell
Place of PublicationRivett
PublisherEvergreen Publishing
Pages86-106
Number of pages21
ISBN (Print)9781921473982
Publication statusPublished - 2008

Fingerprint Dive into the research topics of 'Optimum healthcare image smoothing and restoration based on the two-dimensional ARMA model'. Together they form a unique fingerprint.

  • Cite this

    O'Neill, T., Penm, J. H. W., & Penm, J. (2008). Optimum healthcare image smoothing and restoration based on the two-dimensional ARMA model. In T. J. O'Neill, J. Penm, & R. D. Terrell (Eds.), Collaborative research in electronic healthcare: Bioinformatics, pharmacy informatics and computing (pp. 86-106). Evergreen Publishing.