CAJ | 학술논문

针对传统光流场配准模型会造成图像模糊和细节丢失的问题,提出了一种基于偏微分方程的自适应各向异性配准模型.新模型将具有自适应性的扩散滤波方法引入图像配准,定义具有图像结构保持能力的各向异性扩散函数作为模型的正则项;数据项采用作用于亮度常量假设的非二次惩罚函数以增加模型的稳健性.实验结果表明,新模型能够有效保持图像特征,实现对大脑等复杂图像的有效配准.
침대전통광류장배준모형회조성도상모호화세절주실적문제,제출료일충기우편미분방정적자괄응각향이성배준모형.신모형장구유자괄응성적확산려파방법인입도상배준,정의구유도상결구보지능력적각향이성확산함수작위모형적정칙항;수거항채용작용우량도상량가설적비이차징벌함수이증가모형적은건성.실험결과표명,신모형능구유효보지도상특정,실현대대뇌등복잡도상적유효배준.
A PDE-based adaptive anisotropic model for image registration was proposed to solve the problem that tradi-tional optical flow brings on the image blurring and details losing. The new model introduces adaptivity diffusion filter to image registration, and defines anisotropic diffusion function with the ability of preserving image structure as the regula-rization term; the data term use a non-quadratic penalty function with the assumption in brightness constant to improve the robust of the model. The experimental results show that the model can efficiently protect the image structure and achieve accurate registration of the complex image like brain.