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J Med Imaging (Bellingham). 2017 Jan;4(1):014003. doi: 10.1117/1.JMI.4.1.014003. Epub 2017 Feb 28.

Formulation of image fusion as a constrained least squares optimization problem.

Author information

1
Stanford University , Department of Electrical Engineering, Stanford, California, United States.
2
Stanford University , Pre-Collegiate Summer Institutes, Stanford, California, United States.
3
California State University in Northridge , Department of Mathematics, Northridge, California, United States.

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.

KEYWORDS:

convex optimization; image fusion; medical imaging

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