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

Tile-Based Two-Dimensional Phase Unwrapping for Digital Holography Using a Modular Framework

A variety of physical and biomedical imaging techniques, such as digital holography, interferometric synthetic aperture radar (InSAR), or magnetic resonance imaging (MRI) enable measurement of the phase of a physical quantity additionally to its amplitude. However, the phase can commonly only be measured modulo 2π, as a so called wrapped phase map. Phase unwrapping is the process of obtaining the underlying physical phase map from the wrapped phase. Tile-based phase unwrapping algorithms operate by first tessellating the phase map, then unwrapping individual tiles, and finally merging them to a continuous phase map. They can be implemented computationally efficiently and are robust to noise. However, they are prone to failure in the presence of phase residues or erroneous unwraps of single tiles. We tried to overcome these shortcomings by creating novel tile unwrapping and merging algorithms as well as creating a framework that allows to combine them in modular fashion. To increase the robustness of the tile unwrapping step, we implemented a model-based algorithm that makes efficient use of linear algebra to unwrap individual tiles. Furthermore, we adapted an established pixel-based unwrapping algorithm to create a quality guided tile merger. These original algorithms as well as previously existing ones were implemented in a modular phase unwrapping C++ framework. By examining different combinations of unwrapping and merging algorithms we compared our method to existing approaches. We could show that the appropriate choice of unwrapping and merging algorithms can significantly improve the unwrapped result in the presence of phase residues and noise. Beyond that, our modular framework allows for efficient design and test of new tile-based phase unwrapping algorithms. The software developed in this study is freely available

Un nuovo algoritmo di Phase Unwrapping basato sulla crescita competitiva di regioni

The phase unwrapping problem arises every time one has to analyse experimental data acquired by means of techniques based on periodical phenomena, from the opto-interferometric ones to some geologycal or medical techniques. This paper presents a new phase unwrapping algorithm based on the competitive region growing: starting from an equipotential condition, it makes an area to absorb its neighbours by mean of a quality parameter based on the extension and the coherency of the regions. After a short description of the phase unwrapping problem and of some of the most used techniques, this paper describes the working principle of the algorithm and the implementation details. The paper ends with some examples of applications of the algorithm on synthetic images

Global Gradient-Based Phase Unwrapping Algorithm for Increased Performance in Wavefront Sensing

As the reliance on satellite data for military and commercial use increases, more effort must be exerted to protect our space-based assets. In order to help increase our space domain awareness (SDA), new approaches to ground-based space surveillance via wavefront sensing must be adopted. Improving phase-unwrapping algorithms in order to assist in phase retrieval methods is one way of increasing the performance in current adaptive optics (AO) systems. This thesis proposes a new phase-unwrapping algorithm that uses a global, gradient-based technique to more rapidly identify and correct for areas of phase wrapping during particular phase retrieval methods. This is beneficial in regard to the speed and accuracy within which a wrapped phase estimate is unwrapped using a new algorithm, and doing so without having to change current AO systems or physical setups

Four-Dimensional Earthquake Deformation Using Ant Colony Based Pareto Algorithm

This work has demonstrated a new approach for 4-D phase unwrapping technique to retrieve earthquake displacement due to the fact of Nepal earthquake, 2015. In doing so, conventional InSAR procedures are implemented to two repeat passes of Sentinel-1A satellite data. Further, the three-dimensional phase unwrapping is performed using Flynn algorithm, four-dimensional best-path avoiding singularity loops (4-DBPASL) algorithm and Pareto ant colony algorithm. The study shows that the Pareto ant colony algorithm performed accurately compared to Flynn algorithm, four-dimensional best-path avoiding singularity loops (4-DBPASL) algorithm. In conclusion, integration of the Pareto ant colony algorithm with 4-DBPASLphase unwrapping produce accurate 4-D. Earthquake deformation because of reducing the length of the branch cuts and improving the quality edge of phase unwrapping

SAR Phase Unwrapping Using Path-Based Least-Squares Phase Estimation and Region-Growing with Polynomial-Based Phase Prediction

Differential SAR interferometry (DInSAR) has proven to be a processing approach that is well-suited to precisely identifying large-scale land deformation patterns. This is useful for many environmental monitoring applications, but the speckle noise and temporal decorrelation present in SAR images presents particular challenges in processing SAR images. This research focuses on the phase unwrapping problem, proposing two new approaches: Polynomial-Based Region-Growing Phase Unwrapping (PBRGPU), which expands upon the traditional region-growing approach to phase unwrapping; and Path-Based Least-Squares Phase Unwrapping (PBLSPU), which extends the least-squares phase unwrapping models in a path-based framework. Both algorithms were tested using simulated data and interferograms generated from RADARSAT-2 data. Both approaches significantly reduced the root mean square error compared to the algorithms they build from, and achieved a similar level of performance to the commonly-used SNAPHU algorithm without the need for masking low coherence areas