Robust regularized tomographic imaging with convex projections

Farzad Kamalabadi, Behzad Sharif

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A robust method for tomographic image reconstruction from limited-angle noisy measurements is proposed which builds upon a combination of regularization theory and the method of projections onto convex sets (POCS). Two specific formulations of the proposed method, namely, Tikhonov-POCS and TV-POCS, are introduced and investigated. A statistical framework is developed that provides insight into the behavior of the two algorithms. The inclusion of a reference image is approached by either a coarse reconstruction or a model generated background image. The method is validated in the context of simulations for the reconstruction of highly structured images from partial projections. Results demonstrate significant improvement over conventional regularization methods in situations where the conventional techniques are inadequate.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
Pages205-208
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: Sep 11 2005Sep 14 2005

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing 2005, ICIP 2005
CountryItaly
CityGenova
Period9/11/059/14/05

ASJC Scopus subject areas

  • Engineering(all)

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