On the convergence of generalized simultaneous iterative reconstruction algorithms

In this paper, we generalize the widely used simultaneous block iterative reconstruction algorithm and show that it converges, at a linear rate, to a weighted least-squares and weighted minimum-norm reconstruction. Our theoretical result provides a much simpler proof of the convergence properties ob...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 16(2007), 1 vom: 29. Jan., Seite 1-6
1. Verfasser: Wang, Jiong (VerfasserIn)
Weitere Verfasser: Zheng, Yibin
Format: Aufsatz
Sprache:English
Veröffentlicht: 2007
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Evaluation Study Journal Article
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520 |a In this paper, we generalize the widely used simultaneous block iterative reconstruction algorithm and show that it converges, at a linear rate, to a weighted least-squares and weighted minimum-norm reconstruction. Our theoretical result provides a much simpler proof of the convergence properties obtained by Jiang and Wang and covers a much more general class of algorithms. The frequency domain iterative reconstruction algorithm is then introduced as a special application of our theory 
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