结合张量与互信息的混合模型多模态图像配准方法

A hybrid model combining tensor and mutual information for multi-modal image registration

多模态图像之间存在显著的非线性强度差异,并且图像会因为噪声而退化,因此,多模态图像自动配准是一项具有挑战性的任务。为了解决这两个问题,本文提出一种多模态图像自动配准方法,该方法分为预配准和精配准两个阶段。在预配准阶段,通过改进SIFT算法来大致对齐多模态图像。在精配准阶段,首先,利用块Harris检测器在预配准后的参考图上提取均匀分布的特征点。然后,通过各向异性结构张量捕捉多模态图像中的结构信息来构建特征描述符,该特征描述符对噪声具有稳健性。更进一步,本文结合张量方向平行度和梯度互信息提出了一种相似度准则(tensor orientation and mutual information,TOMI)。最后,本文用多种模态图像(包括Optical,LiDAR,SAR和Map)来评估提出的方法。试验结果表明,本文提出的方法对非线性强度变化和噪声具有较好的稳健性,并且匹配效果优越。

There are significant nonlinear intensity differences between multi-modal images.Moreover,the noise in these images will cause image degradation.Therefore,the automatic registration of multi-modal images is a challenging task.To address the two problems,this paper proposes a multi-modal image automatic registration method,which is divided into two stages:pre-registration and fine registration.In the pre-registration stage,an improved SIFT algorithm is used to roughly align multi-modal images.In the fine registration stage,the block Harris detector is first used to extract evenly distributed feature points on the pre-registered reference image.Then,the structure information in the multi-modal images is captured by the anisotropic structure tensor to construct a feature descriptor,which is robust to noise.Furthermore,a similarity criterion named TOMI(tensor orientation and mutual information)is proposed combining the tensor orientation parallelism and gradient mutual information.Finally,Multi-modal images(including Optical,LiDAR,SAR,and Map data)are used to evaluate the proposed algorithm.The experimental results show that the method proposed in this paper is robust to nonlinear intensity differences and noise,and the matching effect is superior.