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Globally convergent iterative numerical schemes for nonlinear variational image smoothing and segmentation on a multiprocessor machine

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3 Author(s)
Heers, J. ; LaVision GmbH, Gottingen, Germany ; Schnorr, C. ; Stiehl, H.S.

We investigate several iterative numerical schemes for nonlinear variational image smoothing and segmentation implemented in parallel. A general iterative framework subsuming these schemes is suggested for which global convergence irrespective of the starting point can be shown. We characterize various edge-preserving regularization methods from the image processing literature involving auxiliary variables as special cases of this general framework. As a by-product, global convergence can be proven under conditions slightly weaker than these stated in the literature. Efficient Krylov subspace solvers for the linear parts of these schemes have been implemented on a multiprocessor machine. The performance of these parallel implementations has been assessed and empirical results concerning convergence rates and speed-up factors are reported

Published in:

Image Processing, IEEE Transactions on  (Volume:10 ,  Issue: 6 )

Date of Publication:

Jun 2001

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