Modified joint transform correlator binarized by error-diffusion. I. Spatially constant noise-dependent range limit.

Two error-diffusion-based binarization methods for joint transform correlator configurations, which adaptively take into account the effects of input-additive white Gaussian noise, are analyzed. Before binarization, the operations performed upon the joint power spectrum are either truncation and normalization or subtraction of a noise pedestal followed by truncation and normalization. The noise-pedestal value is defined as the measurable estimate of the noise power spectral density. Truncation and normalization are carried out with a spatially constant noise-dependent range limit, based on the statistical properties of the noise, and the noise-pedestal value. All required parameters, dependent on the input-noise level, can be measured from the joint power spectrum distribution and are updated for every new input scene. A computer-simulation comparison of correlation-peak characteristics demonstrates the advantages of the suggested approaches. Optical experiments with compatible results are also presented.

[1]  E. Marom,et al.  Error-diffusion binarization for joint transform correlators. , 1993, Applied optics.

[2]  J. Horner,et al.  Single spatial light modulator joint transform correlator. , 1989, Applied optics.

[3]  Mischa Schwartz,et al.  Information transmission, modulation, and noise , 1959 .

[4]  E Barnard,et al.  Optical correlation CGHs with modulated error diffusion. , 1989, Applied optics.

[5]  O. Bryngdahl,et al.  Computer-generated holograms with pulse-density modulation , 1984 .

[6]  B. Javidi,et al.  Binary nonlinear joint transform correlation with median and subset median thresholding. , 1991, Applied optics.

[7]  Joseph L. Horner,et al.  1-F Binary Joint Transform Correlator. , 1990 .

[8]  D P Casasent,et al.  Optical projection correlations. , 1988, Applied optics.

[9]  B. Javidi,et al.  Joint transform image correlation using a binary spatial light modulator at the Fourier plane. , 1988, Applied optics.

[10]  R Eschbach,et al.  Comparison of error diffusion methods for computer-generated holograms. , 1991, Applied optics.

[11]  Francis T. S. Yu,et al.  Adaptive real-time pattern recognition using a liquid crystal TV based joint transform correlator. , 1987, Applied optics.

[12]  H H Arsenault,et al.  Statistical performance of the circular harmonic filter for rotation-invariant pattern recognition. , 1983, Applied optics.

[13]  J. Goodman,et al.  A technique for optically convolving two functions. , 1966, Applied optics.

[14]  Etienne Barnard,et al.  Optimal error diffusion for computer-generated holograms , 1988 .

[15]  B Javidi,et al.  Multiple-object binary joint transform correlation using multiple-level threshold crossing. , 1991, Applied optics.

[16]  David L. Flannery,et al.  Design elements of binary joint transform correlation and selected optimization techniques , 1992 .

[17]  E Marom,et al.  Modified joint transform correlator binarized by error diffusion. II. Spatially variant range limit. , 1994, Applied optics.

[18]  B. Javidi.  Nonlinear joint power spectrum based optical correlation. , 1989, Applied optics.

[19]  Francis T. S. Yu,et al.  A real-time programmable joint transform correlator , 1984 .

[20]  Steven K. Rogers,et al.  New binarization techniques for joint transform correlation , 1990 .

[21]  Frank Wyrowski,et al.  Quantization noise in pulse density modulated holograms , 1988 .