A Robust Discontinuous Phase Unwrapping Based on Least-Squares Orientation Estimator

Abstract

Weighted least-squares (WLS) phase unwrapping is widely used in optical engineering. However, this technique still has issues in coping with discontinuity as well as noise. In this paper, a new WLS phase unwrapping algorithm based on the least-squares orientation estimator (LSOE) is proposed to improve phase unwrapping robustness. Specifically, the proposed LSOE employs a quadratic error norm to constrain the distance between gradients and orientation vectors. The estimated orientation is then used to indicate the wrapped phase quality, which is in terms of a weight mask. The weight mask is calculated by post-processing, including a bilateral filter, STDS, and numerical relabeling. Simulation results show that the proposed method can work in a scenario in which the noise variance is 1.5. Comparisons with the four WLS phase unwrapping methods indicate that the proposed method provides the best accuracy in terms of segmentation mean error under the noisy patterns.

Target Localization and Grasping of Parallel Robots with Multi-Vision Based on Improved RANSAC Algorithm

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A gamma self‐correction method via chord distribution coding in fringe projection profilometry

“…It should be noted that if the projector's gamma is directly calculated after the 3D reconstruction, the occluded object will modulate the fringe phase. Therefore, the least‐squares orientation estimator method [10] is employed to segment the object and to obtain the object‐removed wrapped phases. To eliminate random error, the average gamma is obtained from multiple object‐removed wrapped phases.…”