【摘要】 互信息是图像配准技术中广泛应用的一种相似性度量方法。传统的互信息方法中仅仅考虑了图像像素的灰度信息,而没有考虑像素之间的空间位置关系。因此,空间信息的缺乏导致了传统方法鲁棒性较差。在讨论了高阶熵的基本概念之后,将像素邻域均值作为高阶熵的第二维变量,由此加入空间信息。实验结果证明,该方法具有很强的抗噪声能力,能够使配准曲线更加平滑,从而避免在搜索过程中陷入局部极值。更多还原

【Abstract】 Mutual information has been widely used in image registration as an effective similarity measure.However,standard mutual information only considers the pixels’ intensity value but ignores their spatial relationship.Therefore,the lack of spatial information leads to the poor robustness of the method.In this paper,the mean value of each pixel’s neighborhood has been incorporated as the second term of the high-order entropy.Thus,the spatial information can be successfully added into the mutual information.The experimental results show that the method has good performance against noise compared with the standard mutual information.The smoothness of the curve for registration function can avoid the searching trapping into the local minimum which often leads the registration to failure.更多还原