基于分块信息熵和特征尺度的图像配准算法

针对当前图像配准技术中特征点的检测和匹配存在的问题，提出了一种基于分块信息熵和特征尺度的图像配准算法.通过对图像进行分块，结合每块图像的信息熵，改善了Harris-Laplace算子提取的特征点分布过于集中的问题.通过比较角点响应函数的值，剔除了特征点中的冗余点.通过结合特征点的尺度信息、Hu矩和双向匹配策略，提高了初始匹配点对的准确率.仿真结果表明，改进的配准算法可以实现高精度的图像配准，对图像的几何变换具有很强的鲁棒性. 

An Image Registration Algorithm Based on Block Information Entropy and Characteristic Scale

To solve the problem of feature point detection and matching in the image registration techniques at present, an improved image registration algorithm was proposed based on block information entropy and characteristic scale. By dividing the image into blocks and combining the information entropy of each block, the concentrated distribution problem of feature points extracted by the Harris-Laplace operator was improved. By comparing the value of corner response function, redundant points in the feature points were eliminated. By combining the scale information of feature points with Hu moments and bidirectional matching strategy, the accuracy of the initial matching points was enhanced. Simulation shows that the improved registration algorithm can realize high-precision image registration and possesses strong robustness with the geometric transformation of image.