针对激光散斑干涉图像在局部高密度噪声和拉线区域中,最小二乘解包裹算法存在过度平滑、迭代次数多和运行时间长等问题,提出一种新的算法。该算法基于散斑干涉图像近似服从周期性的抛物线分布的规律,首先采用两次矩阵变换,对噪声所在的坐标点进行锁定;继而利用掩模技术并结合二维离散余弦变换和Picard迭代方法,对噪声的传播进行抑制,从而获得平滑的图像。实验结果表明:激光散斑干涉测量对局部高密度噪声很敏感,经过平滑优化后,所提算法较传统最小二乘迭代算法具有更少的迭代次数和更短的计算时间,对单根拉线、单个噪声和变形干扰下的干涉测量的识别率高达96%,精度优于传统算法,具有很高的工程应用价值。

In this paper, we propose an improved least squares unwrapping algorithm. This algorithm is aimed at solving the problems associated with smooth transition, large number of iterations, and long running time of least squares unwrapping in local high-density noise and wire-drawing regions of laser speckle interference images. This algorithm is based on the law that the speckle interference image approximately obeys the periodic parabolic distribution. First, the coordinate points where the noise is located are locked using two matrix transformations. Then use the mask technology and combine the two-dimensional discrete cosine transform and Picard iterative method to suppress the propagation of noise, so as to obtain smooth images. The experimental results show that laser speckle interferometry is very sensitive to local high-density noise. Thus, the proposed algorithm has fewer iterations and shorter calculation time during image smoothing and optimization compared with the traditional least-squares iterative algorithms. The recognition rate of interferometry under single noise and deformation interference is approximately 96%, and the accuracy is better than traditional algorithms, which has high engineering application value.