Nonrigid image registration plays an important role in the field of computer vision and medical application. The methods based on Demons algorithm for image registration usually use intensity difference as similarity criteria. However, intensity based methods can not preserve image texture details well and are limited by local minima. In order to solve these problems, we propose a Gabor feature based LogDemons registration method in this article, called GFDemons. We extract Gabor features of the registered images to construct feature similarity metric since Gabor filters are suitable to extract image texture information. Furthermore, because of the weak gradients in some image regions, the update fields are too small to transform the moving image to the fixed image correctly. In order to compensate this deficiency, we propose an inertial constraint strategy based on GFDemons, named IGFDemons, using the previous update fields to provide guided information for the current update field. The inertial constraint strategy can further improve the performance of the proposed method in terms of accuracy and convergence. We conduct experiments on three different types of images and the results demonstrate that the proposed methods achieve better performance than some popular methods.

中文翻译：

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基于 Gabor 特征的 LogDemons，具有用于非刚性图像配准的惯性约束。


非刚性图像配准在计算机视觉和医学应用领域发挥着重要作用。基于恶魔算法的图像配准方法通常使用强度差作为相似性标准。然而，基于强度的方法不能很好地保留图像纹理细节，并且受到局部最小值的限制。为了解决这些问题，我们在本文中提出了一种基于 Gabor 特征的 LogDemons 注册方法，称为 GFDemons。由于 Gabor 滤波器适合提取图像纹理信息，因此我们提取配准图像的 Gabor 特征来构造特征相似度度量。此外，由于某些图像区域的梯度较弱，更新场太小而无法正确地将运动图像变换为固定图像。为了弥补这一缺陷，我们提出了一种基于GFDemons的惯性约束策略，名为IGFDemons，利用先前的更新字段为当前的更新字段提供引导信息。惯性约束策略可以进一步提高所提方法在精度和收敛性方面的性能。我们对三种不同类型的图像进行了实验，结果表明所提出的方法比一些流行的方法取得了更好的性能。