结合图像信息熵和特征点的图像配准方法

Improved image registration using feature points combined with image entropy

在分析当前主要的图像配准技术之后,针对图像特征点的分布和同名点的匹配问题,提出了结合图像信息熵和特征点的图像配准方法。首先对图像进行一定程度的分块,根据信息论的方法,计算每一块的信息熵,信息熵的大小基本反映了各个模块的纹理变换情况。然后根据各个模块的信息熵大小,进行图像的粗匹配。之后在各个模块提取出一定数目的特征点,信息熵大,纹理信息丰富,选取的特征点就相应较多,反之则纹理信息变化不大,选取的特征点数目较少。最后根据这些具有代表性的同名点进行精确匹配。为验证该方法的有效性,对两幅图像进行传统方法和改进的图像配准方法的比较。

By analyzing the major image registration techniques at present, a new image registration method based on image entropy on account of the distribution issue of feature point and the registration of corresponding points was introduced. First, the image was divided into blocks to a certain extent and the image entropy of each block, which reflected the texture transformation within the block, was computed according to the information theory. The rough-match was then made on the basis of the computed image entropy. After that, a certain number of feather points were extracted from each block. The more information content the block had, the more abundant the texture became and so the larger extraction number we got. The precise match was made with these typical corresponding points. To demonstrate the validity of the proposed method, the improved image registration technique was compared to conventional methods on same images.