基于多景深融合模型的显微三维重建方法

摘要

A new micro 3D reconstruction method based on multi-focus image fusion model was proposed which overcame the disadvantage of low efficiency in the processing of the laser scanning confocal microscopy metal samples. In the method, the micro 3D reconstruction problem was transformed into a two-dimensional image fu-sion problem. The whole process can be divided into three stages, including image fusion, optimal matching and iterative recovery stages. In the first phase, a novel microscopy image fusion algorithm based onm-PCNN in non-subsampled Contourlet transform (NSCT) domain was proposed, in which details of each original image can be retained to the first fusion result. In the next phase, the second fusion result and the height map were obtained by an optimal matching algorithm based on the correlation coefficient of regional image. In the last phase, an en-ergy functional model was defined to eliminate the false information in the second fusion result. Minimization of the energy functional ensured that the second fusion result converge to the first fusion result and the height map converge to the 3D reconstruction result. The proposed method was applied to 3D reconstruction of micro-scale metal samples, and the results suggested that compared with the laser scanning confocal microscopy method, the proposed method has higher efficiency and better noise resistance.%鉴于激光共聚焦三维重建技术在金属样本的显微三维重建过程中具有重建速度慢的不足,在保证重建精度的前提下,提出一种基于多景深融合模型的显微三维重建方法来提升重建效率.该方法将三维重建问题转化为二维图像景深融合问题,首先提出一种基于非降采样Contourlet变换与多态脉冲耦合神经网络的图像融合方法,对不同景深图像序列运用该方法进行融合操作,得到的初代融合图像最大程度保留了原始图像信息;然后提出一种基于相关系数的区域图像匹配方法,得到以原始图像为基础的二次融合图像和初代高度映射图;最后定义一个能量泛函模型以消除二次融合图像中存在的假点信息,其极小化过程保证二次融合图像收敛于初代融合图像,同时初代高度映射图按照相同的方式演化,迭代结束即完成三维重建.运用文中方法对微尺度金属样本进行三维重建实验的结果表明,相比于激光共聚焦三维重建,该方法在保证三维重建结果具有较高精度与较好抗噪性的基础上,提升了重建效率.