Image regularization by nonnegatively constrained Conjugate Gradient

P. Favati, G. Lotti, O. Menchi and F. Romani

Applied Mathematics and Computation, 2018, vol. 327, issue C, 35-45

Abstract: In the image reconstruction context the nonnegativity of the computed solution is often required. Conjugate Gradient (CG), used as a reliable regularization tool, may give solutions with negative entries, particularly 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 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.

Keywords: Image reconstruction; Conjugate Gradient; Nonnegativity constraints (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:327:y:2018:i:c:p:35-45

DOI: 10.1016/j.amc.2018.01.011

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