A fringe analysis algorithm for determination of slope, curvature, and twist from a single fringe pattern in digital speckle-shearing interferometry is proposed. A method for estimation of biased curvature and twist maps from fringe orientation and fringe density maps is employed. The curvature and twist maps obtained are further processed by B-spline interpolation to achieve high quality curvature and twist maps. A derivative-based regularized phase tracker (RPT) utilizes these predetermined curvature and twist maps for determination of a slope map from a single shearography fringe pattern. The proposed model requires less computational time and it overcomes the limitations of the RPT model. The method is validated with an experimental fringe pattern. The results show that this method is robust against speckle noise and it is able to retrieve accurate slope, curvature, and twist maps from a single shearography fringe pattern.
Phase retrieval techniques like phase-shifting method and Fourier-transform technique provides accurate phase
distribution for static measurements. However, it is extremely difficult to use these techniques for dynamic measurement.
In this paper, an automated fringe analysis technique to retrieve phase distribution from single fringe pattern is proposed.
The proposed method uses Teager-Hilbert-Huang transform, which is based on empirical mode decomposition (EMD),
vortex operator (VO) and Teager energy operator (TEO) for fringe demodulation. The proposed method is suitable for
both static and dynamic measurements. In this method, a fringe pattern is normalized using EMD and VO generates a
complex field of the signal. Finally TEO is used to obtain the phase and its phase derivatives map. Unlike traditional
phase retrieval algorithms, this method provides unwrapped phase derivatives directly. Hence there is no need for a
separate phase unwrapping process. The proposed method is validated using simulated fringe patterns and experimental
data obtained from electronic speckle pattern interferometry (ESPI). The results show that this method determines the
phase map and its derivatives from the single fringe pattern effectively.
Compressive sensing (CS) is one of the latest signal processing techniques, which facilitates to reconstruct a complete signal from a small number of randomly chosen signal samples. It has been shown that CS can be applied successfully in digital holography. In this paper, a novel approach has been suggested for holographic reconstruction using CS technique for determining displacement and its derivative maps. Off-axis lensless Fourier holography configuration is used to capture a digital hologram and only Fourier transform is used for phase retrieval. Hence the Fourier transform is used as a CS operator in the proposed method and NESTA algorithm is used for signal reconstruction. Speckle noise should be filtered from phase maps for phase unwrapping process. Filtering speckle noise for the phase maps with high fringe density is not a better solution because high density fringes will be worn out while filtering. However, the method suggested in this paper is able to produce super resolution displacement and derivative maps using CS, which overcomes the problem due to high density fringes. In super resolution phase maps, the fringe density will be reduced due to increasing in pixel count. Experimental results demonstrate that the proposed method is able to determine super resolution displacement and derivative maps effectively.
The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.