High resolution spectral analysis of images using the pseudo-Wigner distribution

Several methods for the computation of the discrete Wigner distribution (DWD) through the use of two-dimensional (2-D) analytic signals have been proposed, depending of the direction of the phase shift. Most of the methods cope with the problem of aliasing of the DWD by low-pass prefiltering the spectrum but reducing spatial frequency support. A new method for 2-D DWD free of aliasing and simultaneously increasing the spatial frequency support is proposed through the use of a new analytic signal. In this way, local spectral analysis for small window sizes can be accomplished improving the resolution but reducing cross terms and other interferences inherent in the DWD computation. For the three analytic images used in the comparison, the method proposed here also performs better than the other two for such tasks.

[1]  P. Flandrin,et al.  Detection of changes of signal structure by using the Wigner-Ville spectrum , 1985 .

[2]  Srdjan Stankovic,et al.  On the local frequency, group shift, and cross-terms in some multidimensional time-frequency distributions: a method for multidimensional time-frequency analysis , 1995, IEEE Trans. Signal Process..

[3]  Françoise Peyrin,et al.  Equivalence between two-dimensional analytic and real signal Wigner distributions , 1989, IEEE Trans. Acoust. Speech Signal Process..

[4]  J Bescós,et al.  Image analysis through the Wigner distribution function. , 1989, Applied optics.

[5]  Harry Wechsler,et al.  Segmentation of Textured Images and Gestalt Organization Using Spatial/Spatial-Frequency Representations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  S. Hahn,et al.  Multidimensional complex signals with single-orthant spectra , 1992, Proc. IEEE.

[7]  J. Mayer,et al.  On the Quantum Correction for Thermodynamic Equilibrium , 1947 .

[8]  Ljubisa Stankovic,et al.  Auto-term representation by the reduced interference distributions: a procedure for kernel design , 1996, IEEE Trans. Signal Process..

[9]  William J. Williams,et al.  Improved time-frequency representation of multicomponent signals using exponential kernels , 1989, IEEE Trans. Acoust. Speech Signal Process..

[10]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[11]  Robert Goutte,et al.  On the use of two-dimensional Wigner-Ville distribution for texture segmentation , 1993, Signal Process..

[12]  T. Claasen,et al.  THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS , 1980 .

[13]  W. J. Williams,et al.  Reduced interference distributions: biological applications and interpretations , 1996, Proc. IEEE.

[14]  L. Cohen Generalized Phase-Space Distribution Functions , 1966 .

[15]  Ljubisa Stankovic,et al.  A method for improved distribution concentration in the time-frequency analysis of multicomponent signals using the L-Wigner distribution , 1995, IEEE Trans. Signal Process..

[16]  P. Flandrin,et al.  Méthodes temps-fréquence , 1992 .

[17]  Douglas L. Jones,et al.  A resolution comparison of several time-frequency representations , 1992, IEEE Trans. Signal Process..

[18]  Françoise Peyrin,et al.  The use of a two-dimensional Hilbert transform for Wigner analysis of 2-dimensional real signals , 1990 .

[19]  H. Suzuki,et al.  A method of two‐dimensional spectral analysis using the wigner distribution , 1992 .

[20]  Gabriel Cristóbal,et al.  Image Filtering and Analysis through the Wigner Distribution , 1991 .

[21]  Mingui Sun,et al.  Elimination of cross-components of the discrete pseudo Wigner distribution via image processing , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[22]  Françoise Peyrin,et al.  Equivalence between the two-dimensional real and analytic signal Wigner distributions , 1989 .

[23]  Douglas L. Jones,et al.  A high resolution data-adaptive time-frequency representation , 1990, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[24]  Robert Goutte,et al.  Analysis and comparison of space/spatial-frequency and multiscale methods for texture segmentation , 1995 .

[25]  A. Lohmann,et al.  The wigner distribution function and its optical production , 1980 .

[26]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[27]  H. Wechsler,et al.  Joint spatial/spatial-frequency representation , 1988 .