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.