视觉驱动的变分配准方法

针对传统整体配准模型不能充分顾及局部变形的问题,提出了一种视觉驱动的变分配准方法。该方法在变分模型建立中综合考虑了局部变换、整体平滑和视觉约束,同时兼顾了亮度和对比度差异。首先,基于灰度均方根误差建立配准模型的数据项;其次,为了保证整体平滑,模型采用H¹半范数进行自适应约束;最后,为了保证影像中的空间属性满足视觉的要求,不能出现扭曲变形,采用直线特征进行先验约束。在变分模型求解过程中先利用整个影像估计影像之间的整体变换参数,然后采用小的邻域范围进行局部估计。整个过程在多水平差分框架下迭代求解变换参数,进而兼顾了整体平滑和局部变形。笔者利用ZY-3卫星数据进行了试验,采用目视和量化指标进行了评价,验证了本文方法的优越性。

A Visually Inspired Variational Method for Automatic Image Registration

A visually inspired variational method for automatic image registration is proposed to solve local deformation which traditional global registration model cannot well satisfy.The variational model considers local transformation, global smoothness and visual constraints.To account for intensity variations,we incorporate change of local contrast and brightness into our model.Firstly,the data entry of registration model is built according to the root-mean-square error of intensity;secondly,adaptive constraint using H1 half norm is used to ensure the global smooth in the model;finally,in order to make sure that the spatial attributes of the image satisfy the visual requirements and without distortion,the linear features are used as priori constraints.During the solution of model parameters,the whole image is used to global ly estimate the transformation parameters,and then local estimation of the parameters is taken in a small neighbor.The entire procedure is built upon a multi-level differential framework,and the transformation parameters are calculated iteratively,which can consider both global smoothness and local distortion.To assess the quality of the proposed method,ZY-3 satellite images were used.Visual and quantitative analysis proved that the proposed method can significantly improve the registration precision.