Design of multiresolution binary phase-only synthetic discriminant function filters using multiresolution wavelet analysis approximations of training functions

Paul C. Miller; Steven Angeli; Chris J. Woodruff

doi:10.1117/12.205769

28 March 1995 Design of multiresolution binary phase-only synthetic discriminant function filters using multiresolution wavelet analysis approximations of training functions

Paul C. Miller, Steven Angeli, Chris J. Woodruff

Author Affiliations +

Proceedings Volume 2490, Optical Pattern Recognition VI; (1995) https://doi.org/10.1117/12.205769
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

In conventional approaches to the design of binary phase-only synthetic discriminant function filters the filters are trained on discrete Fourier transforms (DFTs) of the training image, even though DFTs are in fact downsampled approximations of the continuous optical Fourier transform (FT) that is generated in an optical correlator. The justification for this is that one can completely reconstruct the training image from its DFT. In this work however, we show, by use of a realistic correlator simulation, that filters designed by the conventional approach do not give the correct results when implemented in a real correlator. It is shown that multiresolution wavelet analysis approximations (MWA) of the training image FTs should be used for correct filter design. Furthermore, we show that it is possible to design filters whose resolution is reduced with respect to the conventional case, but whose performance is comparable, using the MWA approach. Finally, the performance of the filters trained on MWA approximations was found to be vastly superior in nearly all cases to the equivalent filters trained on conventional downsampled approximations.

Citation Download Citation

Paul C. Miller, Steven Angeli, and Chris J. Woodruff "Design of multiresolution binary phase-only synthetic discriminant function filters using multiresolution wavelet analysis approximations of training functions", Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); https://doi.org/10.1117/12.205769

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