Image Processing

Formulation of image fusion as a constrained least squares optimization problem

[+] Author Affiliations
Nicholas Dwork, John M. Pauly

Stanford University, Department of Electrical Engineering, Stanford, California, United States

Eric M. Lasry

Stanford University, Pre-Collegiate Summer Institutes, Stanford, California, United States

Jorge Balbás

California State University in Northridge, Department of Mathematics, Northridge, California, United States

J. Med. Imag. 4(1), 014003 (Feb 28, 2017). doi:10.1117/1.JMI.4.1.014003
History: Received October 11, 2016; Accepted February 8, 2017
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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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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Nicholas Dwork ; Eric M. Lasry ; John M. Pauly and Jorge Balbás
"Formulation of image fusion as a constrained least squares optimization problem", J. Med. Imag. 4(1), 014003 (Feb 28, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.1.014003


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