Extended Depth-range Dual-wavelength Interferometry Based on Iterative Two-step Temporal Phase-unwrapping

Phase retrieval is one of the most challenging processes in many interferometry techniques. To promote the phase retrieval, Xu et. al [X. Xu, Y. Wang, Y. Xu, W. Jin. 2016] proposed a method based on dual-wavelength interferometry. However, the phase-difference brings large noise due to its low sensitivity and signal-to-noise ratio (SNR). Beside, special phase shifts are required in Xu's method. In the light of these problems, an extended depth-range dual-wavelength phase-shifting interferometry is proposed. Firstly, the least squares algorithm is utilized to retrieve the single-wavelength phase from a sequence of N-frame simultaneous phase-shifting dual-wavelength interferograms (SPSDWI) with random phase shifts. Then the phase-difference and phase-sum are calculated from the wrapped phases of single wavelength, and the iterative two-step temporal phase-unwrapping is introduced to unwrap the phase-sum, which can extend the depth-range and improve the sensitivity. Finally, the height of objects is achieved. Simulated experiments are conducted to demonstrate the superb precision and overall performance of the proposed method.Comment: 21 pages, 19 figure

Dynamic 3D shape measurement based on the phase-shifting moir\'e algorithm

In order to increase the efficiency of phase retrieval,Wang proposed a high-speed moire phase retrieval method.But it is used only to measure the tiny object. In view of the limitation of Wang method,we proposed a dynamic three-dimensional (3D) measurement based on the phase-shifting moire algorithm.First, four sinusoidal fringe patterns with a pi/2 phase-shift are projected on the reference plane and acquired four deformed fringe patterns of the reference plane in advance. Then only single-shot deformed fringe pattern of the tested object is captured in measurement process.Four moire fringe patterns can be obtained by numerical multiplication between the the AC component of the object pattern and the AC components of the reference patterns respectively. The four low-frequency components corresponding to the moire fringe patterns are calculated by the complex encoding FT (Fourier transform) ,spectrum filtering and inverse FT.Thus the wrapped phase of the object can be determined in the tangent form from the four phase-shifting moire fringe patterns using the four-step phase shifting algorithm.The continuous phase distribution can be obtained by the conventional unwrapping algorithm. Finally, experiments were conducted to prove the validity and feasibility of the proposed method. The results are analyzed and compared with those of Wang method, demonstrating that our method not only can expand the measurement scope, but also can improve accuracy.Comment: 14 pages,5 figures. ams.or

One shot profilometry using iterative two-step temporal phase-unwrapping

This paper reviews two techniques that have been recently published for 3D profilometry and proposes one shot profilometry using iterative two-step temporal phase-unwrapping by combining the composite fringe projection and the iterative two-step temporal phase unwrapping algorithm. In temporal phase unwrapping, many images with different frequency fringe pattern are needed to project which would take much time. In order to solve this problem, Ochoa proposed a phase unwrapping algorithm based on phase partitions using a composite fringe, which only needs projecting one composite fringe pattern with four kinds of frequency information to complete the process of 3D profilometry. However, we found that the fringe order determined through the construction of phase partitions tended to be imprecise. Recently, we proposed an iterative two-step temporal phase unwrapping algorithm, which can achieve high sensitivity and high precision shape measurement. But it needs multiple frames of fringe images which would take much time. In order to take into account both the speed and accuracy of 3D shape measurement, we get a new, and more accurate unwrapping method based on composite fringe pattern by combining these two techniques. This method not only retains the speed advantage of Ochoa's algorithm, but also greatly improves its measurement accuracy. Finally, the experimental evaluation is conducted to prove the validity of the proposed method, and the experimental results show that this method is feasible.Comment: 14 pages, 15 figure

Improved method for phase wraps reduction in profilometry

In order to completely eliminate, or greatly reduce the number of phase wraps in 2D wrapped phase map, Gdeisat et al. proposed an algorithm, which uses shifting the spectrum towards the origin. But the spectrum can be shifted only by an integer number, meaning that the phase wraps reduction is often not optimal. In addition, Gdeisat's method will take much time to make the Fourier transform, inverse Fourier transform, select and shift the spectral components. In view of the above problems, we proposed an improved method for phase wraps elimination or reduction. First, the wrapped phase map is padded with zeros, the carrier frequency of the projected fringe is determined by high resolution, which can be used as the moving distance of the spectrum. And then realize frequency shift in spatial domain. So it not only can enable the spectrum to be shifted by a rational number when the carrier frequency is not an integer number, but also reduce the execution time. Finally, the experimental results demonstrated that the proposed method is feasible.Comment: 16 pages, 15 figures, 1 table. arXiv admin note: text overlap with arXiv:1604.0723

Unsupervised CNN-Based DIC for 2D Displacement Measurement

Digital image correlation method is a non contact deformation measurement technique. Despite years of development, it is still difficult to solve the contradiction between calculation efficiency and seed point quantity.With the development of deep learning, the DIC algorithm based on deep learning provides a new solution for the problem of insufficient calculation efficiency in DIC.All supervised learning DIC methods requires a large set of high quality training set. However, obtaining such a dataset can be challenging and time consuming in generating ground truth. To fix the problem,we propose an unsupervised CNN Based DIC for 2D Displacement Measurement.The speckle image warp model is created to transform the target speckle image to the corresponding predicted reference speckle image by predicted 2D displacement map, the predicted reference speckle image is compared with the original reference speckle image to realize the unsupervised training of the CNN.The network's parameters are optimized using a composite loss function that incorporates both the Mean Squared Error and Pearson correlation coefficient.Our proposed method has a significant advantage of eliminating the need for extensive training data annotations. We conducted several experiments to demonstrate the validity and robustness of the proposed method. The experimental results demonstrate that our method can achieve can achieve accuracy comparable to previous supervised methods. The PyTorch code will be available at the following URL: https://github.com/fead1

Enhanced Dual-frequency Pattern Scheme Based on Spatial-temporal Fringes Method

One of the major challenges of employing a dual-frequency phase-shifting algorithm for phase retrieval is its sensitivity to noise. Yun et. al [H Yun, B Li, S Zhang. 2017] proposed a dual-frequency method based on the Fourier transform profilometry (FTP), yet the low frequency lobes are close to each other for accurate band-pass filtering. In light of this problem, a novel dual-frequency pattern based on the spatial-temporal fringes (STF) method is developed in this paper. Three fringe patterns with two different frequency are required. The low frequency phase is obtained from two low frequency fringe patterns by the STF method, so the signal lobes can be extracted accurately as they are far away from each other. The high frequency phase is retrieved from another fringe pattern without the impact of the DC component. Simulations and experiments are conducted to demonstrate the excellent precision of the proposed method.Comment: 24 pages,39 figure