非刚性图像配准技术在计算机领域中是一个重要的研究方向,也广泛应用在医学、天文和**等领域。非刚性图像配准过程中,往往无法同时解决局部大形变和小形变的问题。针对此问题,结合归一化互信息图像配准的理论,提出基于互信息的Active Demons非刚性图像配准算法。该算法通过引入互信息来提高Active Demons算法的驱动力,同时采用待配准图像和原图像的互信息自适应地调节归一化因子α,再对医学图像、自然图像和合成图像进行测试仿真实验。实验结果表明:所提算法在配准精度和鲁棒性上优于原来的Active Demons算法,可以较好地处理大形变图像并保留其边缘特征,得到更好的配准精度和视觉效果。

Nonrigid image registration technology is an important research direction in the computer field. It is widely used in medicine, astronomy, and military applications. In the process of nonrigid image registration, it is often impossible to simultaneously deal with local large and small deformations. To solve this problem, in combination with the theory of normalized mutual information image registration, an Active Demons nonrigid image registration algorithm based on mutual information is proposed herein. This algorithm improves the driving force of the Active Demons algorithm by introducing mutual information, which is used to register the image. The original image is used to adaptively adjust the normalization factor, and then the algorithm conducts test simulations on medical, natural, and synthetic images. Experiment results show that the proposed algorithm is superior to the original Active Demons algorithm in terms of registration accuracy and robustness. It can process large deformed images and retain their edge features, thereby providing better registration accuracy and visual effects.