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, the fused image provides clinicians with a single frame of the most complete diagnostic information. In this paper, image fusion is formulated as a convex optimization problem which 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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