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Journal of Medical Imaging

Formulation of image fusion as a constrained least squares optimization problem
Author(s): Nicholas Dwork; Eric M. Lasry; John M. Pauly; Jorge Balbás
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Paper 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.

Paper Details

Date Published: 28 February 2017
PDF: 10 pages
J. Med. Img. 4(1) 014003 doi: 10.1117/1.JMI.4.1.014003
Published in: Journal of Medical Imaging Volume 4, Issue 1
Show Author Affiliations
Nicholas Dwork, Stanford Univ. (United States)
Eric M. Lasry, Stanford Univ. (United States)
John M. Pauly, Stanford Univ. (United States)
Jorge Balbás, California State Univ., Northridge (United States)


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