Image processing algorithms for the analysis of phase-shifted speckle interference patterns

Vrooman, Henri A.; Maas, Ad A. M.

doi:10.1364/ao.30.001636

Cited by 74 publications

(21 citation statements)

References 8 publications

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“…Phase unwrapping techniques for MRI have been presented in [4], [12], [30], and [54]. In the field of optical interferometry, several methods were published in [28], [46], [47], [51], and [57]. Reference [34] includes a phase unwrapping algorithm in the context of diffraction tomography.…”

Section: B the "Phase Unwrapping" Approachmentioning

confidence: 99%

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Absolute phase image reconstruction: a stochastic nonlinear filtering approach

Leitao

Figueiredo

1998

IEEE Trans. on Image Process.

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Abstract-This paper formulates and proposes solutions to the problem of estimating/reconstructing the absolute (not simply modulo-2) phase of a complex random field from noisy observations of its real and imaginary parts. This problem is representative of a class of important imaging techniques such as interferometric synthetic aperture radar, optical interferometry, magnetic resonance imaging, and diffraction tomography. We follow a Bayesian approach; then, not only a probabilistic model of the observation mechanism, but also prior knowledge concerning the (phase) image to be reconstructed, are needed. We take as prior a nonsymmetrical half plane autoregressive (NSHP AR) Gauss-Markov random field (GMRF). Based on a reduced order state-space formulation of the (linear) NSHP AR model and on the (nonlinear) observation mechanism, a recursive stochastic nonlinear filter is derived. The corresponding estimates are compared with those obtained by the extended Kalman-Bucy filter, a classical linearizing approach to the same problem. A set of examples illustrate the effectiveness of the proposed approach.

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Section: B the "Phase Unwrapping" Approachmentioning

confidence: 99%

“…• Optical interferometry, where (absolute) phase differences are used to obtain information, such as shape, displacement, or vibration, of a surface under inspection; see, e.g., [28], [51] and [57].…”

mentioning

confidence: 99%

Absolute phase image reconstruction: a stochastic nonlinear filtering approach

Leitao

Figueiredo

1998

IEEE Trans. on Image Process.

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“…K3 comes from the linear least-squares fit of a plane to the pixels in a rectangular neighborhood, and it gives more importance to the pixels far from the center. 9 Finally, the K4 weights are equal to the square of the distance from the center.…”

Section: Fringe Simulation and Derivative Kernel Selectionmentioning

confidence: 99%

Automatic processing in moiré deflectometry by local fringe direction calculation

1998

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An algorithm for accurately extracting the local fringe direction is presented. The algorithm estimates, in the neighborhood of n ϫ n points, the direction of the gradient that points normal to the local fringe direction. The performance of four different derivative kernels is also compared. Since this method is sensitive to noise and variations in background and amplitude, a preprocessing step is used to limit these error sources. The method has been applied to the moiré deflectogram of a spherical and a progressive addition ophthalmic lens, resulting in a map of the refractive power of these lenses. The results are compared with the data obtained with a commercial focimeter. This technique is useful for analyzing the fringe patterns where the fringe direction is variable and must be obtained locally.

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“…A combination of the iteration algorithm and a modified version of the pixel queue unwrapping method of Vrooman and Maas [9] is used to successfully unwrap the phase with little user input. …”

Section: Phase Steppingmentioning

confidence: 99%