Formulation of image fusion as a constrained least squares optimization problem

Nicholas Dwork; Eric M. Lasry; John M. Pauly; Jorge Balbás

doi:10.1117/1.JMI.4.1.014003

28 February 2017 Formulation of image fusion as a constrained least squares optimization problem

Nicholas Dwork, Eric M. Lasry, John M. Pauly, Jorge Balbás

Author Affiliations +

Funded by: National Institute of Health’s, NIH

Journal of Medical Imaging, Vol. 4, Issue 1, 014003 (February 2017). https://doi.org/10.1117/1.JMI.4.1.014003

Abstract

Fusing a lower resolution color image with a higher resolution monochrome image is a common practice in medical imaging. By incorporating spatial context and/or improving the signal-to-noise ratio, it provides clinicians with a single frame of the most complete information for diagnosis. In this paper, image fusion is formulated as a convex optimization problem that avoids image decomposition and permits operations at the pixel level. This results in a highly efficient and embarrassingly parallelizable algorithm based on widely available robust and simple numerical methods that realizes the fused image as the global minimizer of the convex optimization problem.

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Nicholas Dwork, Eric M. Lasry, John M. Pauly, and Jorge Balbás "Formulation of image fusion as a constrained least squares optimization problem," Journal of Medical Imaging 4(1), 014003 (28 February 2017). https://doi.org/10.1117/1.JMI.4.1.014003

Received: 11 October 2016; Accepted: 8 February 2017; Published: 28 February 2017

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