Complex System, Inc., Calgary, Canada.
IEEE Trans Image Process. 2011 May;20(5):1363-72. doi: 10.1109/TIP.2010.2090530. Epub 2010 Nov 1.
Both image registration and fusion can be formulated as estimation problems. Instead of estimating the registration parameters and the true scene separately as in the conventional way, we propose a maximum likelihood approach for joint image registration and fusion in this paper. More precisely, the fusion performance is used as the criteria to evaluate the registration accuracy. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. The expectation maximization algorithm is employed to solve this joint optimization problem. The Cramer-Rao bound (CRB) is then derived. Our experiments use several types of sensory images for performance evaluation, such as visual images, IR thermal images, and hyperspectral images. It is shown that the mean square error of estimating the registration parameters using the proposed method is close to the CRBs. At the mean time, an improved fusion performance can be achieved in terms of the edge preservation measure Q(AB/F), compared to the Laplacian pyramid fusion approach.
图像配准和融合都可以被表述为估计问题。我们提出了一种新的方法，在本文中，我们将图像配准和融合联合起来，而不是像传统方法那样分别估计配准参数和真实场景。更确切地说，融合性能被用作评估配准精度的标准。因此，可以自动调整配准参数，从而可以同时优化融合和配准。期望最大化算法被用来解决这个联合优化问题。然后推导出克拉美罗界（CRB）。我们的实验使用了几种类型的传感器图像进行性能评估，例如视觉图像、IR 热图像和高光谱图像。结果表明，使用所提出的方法估计配准参数的均方误差接近 CRB。同时，与拉普拉斯金字塔融合方法相比，在边缘保持度量 Q(AB/F)方面，可以实现更好的融合性能。
