Dwork Nicholas, Lasry Eric M, Pauly John M, Balbás Jorge
Stanford University , Department of Electrical Engineering, Stanford, California, United States.
Stanford University , Pre-Collegiate Summer Institutes, Stanford, California, United States.
J Med Imaging (Bellingham). 2017 Jan;4(1):014003. doi: 10.1117/1.JMI.4.1.014003. Epub 2017 Feb 28.
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.
将低分辨率彩色图像与高分辨率单色图像融合是医学成像中的常见做法。通过纳入空间上下文和/或提高信噪比，它为临床医生提供了用于诊断的最完整信息的单帧图像。在本文中，图像融合被表述为一个凸优化问题，该问题避免了图像分解并允许在像素级别进行操作。这产生了一种基于广泛可用的强大且简单的数值方法的高效且易于并行化的算法，该算法将融合图像实现为凸优化问题的全局最小化器。
