Enforcing nonnegativity in image reconstruction algorithms

James G. Nagy; Zdenek Strakos

doi:10.1117/12.402439

4 October 2000 Enforcing nonnegativity in image reconstruction algorithms

James G. Nagy, Zdenek Strakos

Proceedings Volume 4121, Mathematical Modeling, Estimation, and Imaging; (2000) https://doi.org/10.1117/12.402439
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

In image restoration and reconstruction applications, unconstrained Krylov subspace methods represent an attractive approach for computing approximate solutions. They are fast, but unfortunately they do not produce approximate solutions preserving nonnegativity. As a consequence the error of the computed approximate solution can be large. Enforcing a nonnegativity constraint can produce much more accurate approximate solutions, but can also be computationally expensive. This paper considers a nonnegativity constrained minimization algorithm which represents a variant of an algorithm proposed by Kaufman. Numerical experiments show that the algorithm can be more accurate and computationally competitive with unconstrained Krylov subspace methods.

Citation Download Citation

James G. Nagy and Zdenek Strakos "Enforcing nonnegativity in image reconstruction algorithms", Proc. SPIE 4121, Mathematical Modeling, Estimation, and Imaging, (4 October 2000); https://doi.org/10.1117/12.402439

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