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Image regularization by nonnegatively constrained conjugate gradient

In the image reconstruction context the nonnegativity of the computed solution is often required. Conjugate Gradient ({\tt CG}), used as a reliable regularization tool, may give solutions with negative entries, particularly evident when large nearly zero
plateaus are present. The active constraints set, detected by projection  onto the nonnegative orthant, turns out to be largely incomplete leading to poor effects on the accuracy of the reconstructed image. In this paper an inner-outer method based on
{\tt CG} is proposed  to compute nonnegative reconstructed images with a strategy which enlarges subsequently the active constraints set. This method appears to be especially suitable for the reconstruction of images having large nearly zero backgrounds. The
numerical experimentation validates the effectiveness of the proposed method when compared to other strategies for nonnegative  reconstruction.

2017

Autori esterni: Grazia Lotti (Dip. di Matematica, Università di Parma), Ornella Menchi (Dip.di Informatica, Università di Pisa), Francesco Romani (Dip. di Informatica, Università di Pisa)
Autori IIT:

Tipo: Rapporto Tecnico
Area di disciplina: Mathematics
IIT TR-04/2017

File: TR 04_2017.pdf

Attività: Metodi numerici per problemi di grandi dimensioni