Algebraic phase unwrapping for functional data analytic estimations—Extensions and stabilizations

The phase unwrapping, which is a problem to reconstruct the continuous phase function of an unknown complex function from its finite observed samples, has been a key for estimating useful physical quantity in many signal and image processing applications. In the light of the functional data analysis, it is natural to estimate first the unknown complex function by a certain piecewise complex polynomial and then to compute the exact unwrapped phase of the piecewise complex polynomial with the algebraic phase unwrapping algorithms. In this paper, we propose several useful extensions and numerical stabilization of the algebraic phase unwrapping along the real axis. The proposed extensions include (i) removal of a certain critical assumption premised in the original algebraic phase unwrapping, and (ii) algebraic phase unwrapping for a pair of bivariate polynomials. Moreover, in order to resolve certain numerical instabilities caused by the coefficient growth in an inductive step in the original algorithm, we propose to compute directly a certain subresultant sequence without passing through the inductive step.

[1]  J Szumowski,et al.  Phase unwrapping in the three-point Dixon method for fat suppression MR imaging. , 1994, Radiology.

[2]  Mark D. Pritt,et al.  Least-squares two-dimensional phase unwrapping using FFT's , 1994, IEEE Trans. Geosci. Remote. Sens..

[3]  J. Marron,et al.  Unwrapping algorithm for least-squares phase recovery from the modulo 2π bispectrum phase , 1990 .

[4]  Dennis C. Ghiglia,et al.  Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software , 1998 .

[5]  Grace Wahba,et al.  8. Fredholm Integral Equations of the First Kind , 1990 .

[6]  Isao Yamada,et al.  Algebraic phase unwrapping along the real axis: extensions and stabilizations , 2015, Multidimens. Syst. Signal Process..

[7]  R.E. Hansen,et al.  Signal processing for AUV based interferometric synthetic aperture sonar , 2003, Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492).

[8]  Gary H. Glover,et al.  Phase unwrapping of MR phase images using Poisson equation , 1995, IEEE Trans. Image Process..

[9]  A C C Gibbs,et al.  Data Analysis , 2009, Encyclopedia of Database Systems.

[10]  B. Silverman,et al.  Some Aspects of the Spline Smoothing Approach to Non‐Parametric Regression Curve Fitting , 1985 .

[11]  C. Werner,et al.  Satellite radar interferometry: Two-dimensional phase unwrapping , 1988 .

[12]  L. Schumaker Spline Functions: Basic Theory , 1981 .

[13]  I. Vaughan L. Clarkson,et al.  Frequency Estimation by Phase Unwrapping , 2010, IEEE Transactions on Signal Processing.

[14]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[15]  Thomas J. Flynn,et al.  TWO-DIMENSIONAL PHASE UNWRAPPING WITH MINIMUM WEIGHTED DISCONTINUITY , 1997 .

[16]  P. Denbigh Signal processing strategies for a bathymetric sidescan sonar , 1994 .

[17]  G. Wahba Spline models for observational data , 1990 .

[18]  Isao Yamada,et al.  A Robust Algebraic Phase Unwrapping Based on Spline Approximation (VLSI設計技術) , 2012 .

[19]  R. Hudgin Wave-front reconstruction for compensated imaging , 1977 .

[20]  L. Ying PHASE UNWRAPPING , 2005 .

[21]  Kitahara Daichi,et al.  A Robust Algebraic Phase Unwrapping Based on Spline Approximation , 2012 .

[22]  Franz Pfeiffer,et al.  X-ray phase imaging with a grating interferometer. , 2005, Optics express.

[23]  P. Cloetens,et al.  Holotomography: Quantitative phase tomography with micrometer resolution using hard synchrotron radiation x rays , 1999 .

[24]  C. Micchelli,et al.  On multivariate -splines , 1989 .

[25]  T. R. Judge,et al.  A review of phase unwrapping techniques in fringe analysis , 1994 .

[26]  M. Sasaki,et al.  Analysis of accuracy decreasing in polynomial remainder sequence with floating-point number coefficients , 1990 .

[27]  H A Zebker,et al.  Phase unwrapping through fringe-line detection in synthetic aperture radar interferometry. , 1994, Applied optics.

[28]  Pedro Negrete-Regagnon,et al.  Practical aspects of image recovery by means of the bispectrum , 1996 .

[29]  G H Glover,et al.  Three‐point dixon technique for true water/fat decomposition with B0 inhomogeneity correction , 1991, Magnetic resonance in medicine.

[30]  L. C. Graham,et al.  Synthetic interferometer radar for topographic mapping , 1974 .

[31]  Joseph F. Traub,et al.  On Euclid's Algorithm and the Theory of Subresultants , 1971, JACM.

[32]  Gene H. Golub,et al.  On direct methods for solving Poisson's equation , 1970, Milestones in Matrix Computation.

[33]  Tateaki Sasaki,et al.  Polynomial remainder sequence and approximate GCD , 1997, SIGS.

[34]  George E. Collins,et al.  Subresultants and Reduced Polynomial Remainder Sequences , 1967, JACM.

[35]  Mario Costantini,et al.  A novel phase unwrapping method based on network programming , 1998, IEEE Trans. Geosci. Remote. Sens..

[36]  Charles V. Jakowatz,et al.  Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach , 1996 .

[37]  Isao Yamada,et al.  Algebraic multidimensional phase unwrapping and zero distribution of complex polynomials-characterization of multivariate stable polynomials , 1998, IEEE Trans. Signal Process..

[38]  N. K. Bose,et al.  Algebraic phase unwrapping and zero distribution of polynomial for continuous-time systems , 2002 .

[39]  Isao Yamada,et al.  High-resolution estimation of the directions-of-arrival distribution by algebraic phase unwrapping algorithms , 2011, Multidimens. Syst. Signal Process..

[40]  Robert J. Noll,et al.  Phase estimates from slope-type wave-front sensors , 1978 .

[41]  Louis A. Romero,et al.  Minimum Lp-norm two-dimensional phase unwrapping , 1996 .

[42]  E CollinsGeorge Subresultants and Reduced Polynomial Remainder Sequences , 1967 .

[43]  David L. Fried,et al.  Least-square fitting a wave-front distortion estimate to an array of phase-difference measurements , 1977 .

[44]  J. R. Buckland,et al.  Unwrapping noisy phase maps by use of a minimum-cost-matching algorithm. , 1995, Applied optics.