【摘要】 针对传统经验模态分解(Empirical mode decomposition,EMD)在边缘易出现分解错误的问题,本文提出一种改进的经验模态分解方法.分别对条纹进行镜像延拓和Gerchberg外插迭代来实现边沿的拓展,有效抑制条纹边沿引起的模态分解错误,提高分解准确度.将改进的EMD分解方式应用于变形结构光条纹图的分析,能有效消除条纹中的背景分布,得到更好的三维面形重建效果.更多还原

【Abstract】 Aiming at the problem of the edge error caused by the traditional empirical mode decomposition(EMD)method,an improved EMD method is proposed for eliminating the decomposition error in the edge zones of the signals in this paper.A mirror extension method and Gerchberg extrapolation iteration method are introduced to eliminate the decomposition error at edges,respectively.The improved methods can effectively suppress the mode decomposition error caused by the signal edges and improve the decomposition accuracy of the EMD.When they are applied to analyze the deformed fringe pattern,a better reconstructed result of 3Dsurface can be obtained because the background component of the fringe is eliminated well.更多还原