A new phase unwrapping algorithm using two digital phase-shifting gratings of different period for fringe projective 3D profile sensor

2007 ◽  
Author(s):  
Jian Luo ◽  
Yi Wang ◽  
Ai Xiong ◽  
Jiahu Yuan
Keyword(s):  
Phase Unwrapping ◽  
Phase Shifting ◽  
Digital Phase ◽  
3D Profile ◽  
New Phase

scholarly journals Multi-Wavelength Digital-Phase-Shifting Moiré Based on Moiré Wavelength

2019 ◽  
Vol 9 (9) ◽  
pp. 1917 ◽  
Author(s):  
Fatemeh Mohammadi ◽  
Jonathan Kofman
Keyword(s):  
Continuous Phase ◽  
Phase Unwrapping ◽  
Phase Shifting ◽  
Surface Shape ◽  
Digital Phase ◽  
Temporal Phase ◽  
Single Pattern ◽  
Multi Wavelength ◽  
Phase Map ◽  
Surface Discontinuities

Multi-wavelength digital-phase-shifting moiré was demonstrated using multiple moiré wavelengths determined by system calibration over the full working depth. The method uses the extended noisy phase map as a reference to unwrap the phase map with a shorter wavelength, and thus achieve a less noisy and more accurate continuous phase map. The moiré wavelength calibration determines a moiré-wavelength to height relationship that permits pixelwise refinement of the moiré wavelength and height during 3D reconstruction. Only a single pattern has to be projected and, thus, a single image captured to compute each phase map with a different wavelength to perform digital-phase-shifting moiré temporal phase unwrapping. Only two captured images are required for two-wavelength phase unwrapping and three captured images for three-wavelength phase unwrapping. The method has been demonstrated in the 3D surface-shape measurement of an object with surface discontinuities and spatially isolated objects.

scholarly journals An Error-Reduction Method for Temporal Phase Unwrapping Based on Double Three-Step Phase-Shifting and Least-Squares Fitting

2012 ◽  
Vol 6-7 ◽  
pp. 76-81
Author(s):  
Yong Liu ◽  
Ding Fa Huang ◽  
Yong Jiang
Keyword(s):  
Least Squares ◽  
Reduction Method ◽  
Intermediate Phase ◽  
Error Reduction ◽  
Phase Unwrapping ◽  
Phase Shifting ◽  
Surface Measurement ◽  
Acquisition Time ◽  
Least Squares Fitting ◽  
Temporal Phase

Phase-shifting interferometry on structured light projection is widely used in 3-D surface measurement. An investigation shows that least-squares fitting can significantly decrease random error by incorporating data from the intermediate phase values, but it cannot completely eliminate nonlinear error. This paper proposes an error-reduction method based on double three-step phase-shifting algorithm and least-squares fitting, and applies it on the temporal phase unwrapping algorithm using three-frequency heterodyne principle. Theoretical analyses and experiment results show that this method can greatly save data acquisition time and improve the precision.

3D InSAR Phase Unwrapping Within the Compressive Sensing Framework

2019 ◽  
pp. 809-841
Author(s):  
Wajih Ben Abdallah ◽  
Riadh Abdelfattah
Keyword(s):  
Compressive Sensing ◽  
General Problem ◽  
Estimation Error ◽  
Curvelet Transform ◽  
Problem Formulation ◽  
Phase Unwrapping ◽  
Interferometric Synthetic Aperture Radar ◽  
Phase Volume ◽  
The Relationship ◽  
New Phase

This chapter presents a new phase unwrapping algorithm for the 3D Interferometric Synthetic Aperture Radar (3D InSAR) volumes. The proposed approach is based on the relationship between the gradient vectors of the observed wrapped phase and the true phase respectively, when the Itoh condition is satisfied. Since this relationship is violated by the residue pixels in the observed wrapped phase, a general problem formulation which takes into account the estimation error due to these residue values is proposed. This approach exploits the temporal inter correlation between the interferometric frames within a compressive sensing framework. The 3D discrete curvelet transform is used in order to ensure a suitable sparse representation of the phase volume. The performance of the proposed 3D phase unwrapping algorithm is tested on simulated and real SAR 3D datasets

3D InSAR Phase Unwrapping within the Compressive Sensing Framework

2017 ◽  
pp. 125-157
Author(s):  
Wajih Ben Abdallah ◽  
Riadh Abdelfattah
Keyword(s):  
Compressive Sensing ◽  
General Problem ◽  
Estimation Error ◽  
Curvelet Transform ◽  
Problem Formulation ◽  
Phase Unwrapping ◽  
Interferometric Synthetic Aperture Radar ◽  
Phase Volume ◽  
The Relationship ◽  
New Phase

This chapter presents a new phase unwrapping algorithm for the 3D Interferometric Synthetic Aperture Radar (3D InSAR) volumes. The proposed approach is based on the relationship between the gradient vectors of the observed wrapped phase and the true phase respectively, when the Itoh condition is satisfied. Since this relationship is violated by the residue pixels in the observed wrapped phase, a general problem formulation which takes into account the estimation error due to these residue values is proposed. This approach exploits the temporal inter correlation between the interferometric frames within a compressive sensing framework. The 3D discrete curvelet transform is used in order to ensure a suitable sparse representation of the phase volume. The performance of the proposed 3D phase unwrapping algorithm is tested on simulated and real SAR 3D datasets

Least Squares Phase Unwrapping Algorithm Based on Topographic Factors

2011 ◽  
Vol 105-107 ◽  
pp. 1876-1879
Author(s):  
Wei Ke Liu ◽  
Gou Lin Liu ◽  
Xiao Qing Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Local Phase ◽  
Complex Signals ◽  
Topographic Factors ◽  
Interference Fringes ◽  
Phase Images ◽  
New Phase ◽  
Accuracy And Stability

The phase of complex signals is wrapped since it can only be measured modulo-2; unwrapping searches for the 2-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing. Weighted least squares phase unwrapping algorithm could avoid errors transmission in the whole phase images, but it could not avoid defect and overlay of interference fringes caused by topographic factors. Therefore, a new phase unwrapping and weights choosing method based on local phase frequency estimate of topographic factors was presented. Experiments show it is an efficient phase unwrapping method which well overcome the defect of under-estimate slopes by least squares algorithm, and has higher accuracy and stability than other methods.

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