基于拉普拉斯金字塔加权熵的散斑区域提取

Speckle Area Extraction Based on Weighted Entropy of Laplacian Pyramid

在数字图像相关计算中,通常需要人为地选择一个散斑区域以限制搜索区域。随着工业自动化的发展,经常需要获得材料表面的实时位移场和应变场,找到一种快速准确的散斑区域提取算法显得尤为重要。二阶梯度熵函数在散斑提取过程中,耗时过长,计算复杂多变的散斑图时不能准确提取散斑区域。针对这些问题,根据散斑分布特征,提出了一种基于拉普拉斯金字塔加权熵的散斑区域提取算法。该算法在降低计算量的同时,可根据不同散斑图的熵值分布直方图灵活地自动确定阈值。研究结果表明:在散斑区域提取过程中,该算法可以较灵活地选择阈值,完成散斑区域的自动提取,同时计算时间可减少90%以上。该算法较二阶梯度熵函数有了较好的改进,基本能实现复杂背景下散斑区域的提取,同时提取速度有了显著的提升。

In digital image correlation calculations,it is usually necessary to manually select a speckle area for limiting the search area.With the development of industrial automation,it is often necessary to obtain the real-time displacement field and strain field of a material surface.It is particularly important to find a fast and accurate speckle area extraction algorithm.There exist the problems that the second-order gradient entropy function takes too much time in the speckle area extraction process and the speckle area cannot be accurately extracted for a complex and variable speckle.In light of these problems and according to the distribution characteristics of speckles,an algorithm for speckle area extraction is proposed,which is based on the weighted entropy of Laplacian pyramid.This algorithm not only reduces the amount of calculation,but also can flexibly and automatically determine the threshold value according to the entropy value distribution histograms of different speckle images.The research results show that in the extraction process of speckle areas,the proposed algorithm can flexibly select the threshold value to complete the automatic extraction of speckle areas,and simultaneously reduces the calculation time by more than 90%.This proposed algorithm is better than the second-order gradient entropy function,which can basically achieve the extraction of speckle areas under complex backgrounds and the extraction speed is significantly improved.