Formulation of image fusion as a constrained least squares optimization problem.
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.
Stanford University , Department of Electrical Engineering, Stanford, California, United States.
Nicholas
N
Dwork
Stanford University , Pre-Collegiate Summer Institutes, Stanford, California, United States.
Eric M
EM
Lasry
Stanford University , Department of Electrical Engineering, Stanford, California, United States.
John M
JM
Pauly
California State University in Northridge , Department of Mathematics, Northridge, California, United States.
Jorge
J
Balbás
convex optimization; image fusion; medical imaging