Open AccessPhotorefractive two-beam coupling joint transform correlator: modeling and performance evaluation
Author(s) -
George Nehmetallah,
Jed Khoury,
Partha P. Banerjee
Publication year - 2016
Publication title -
applied optics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.668
H-Index - 197
eISSN - 2155-3165
pISSN - 1559-128X
DOI - 10.1364/ao.55.004011
Subject(s) - photorefractive effect , clutter , metric (unit) , figure of merit , computer science , optics , coupling (piping) , joint (building) , noise (video) , optical correlator , dynamic range , performance metric , nonlinear system , beam (structure) , algorithm , electronic engineering , physics , artificial intelligence , fourier transform , image (mathematics) , telecommunications , radar , materials science , engineering , architectural engineering , operations management , management , quantum mechanics , economics , metallurgy
The photorefractive two-beam coupling joint transform correlator combines two features. The first is embedded semi-adaptive optimality, which weighs the correlation against clutter and noise in the input, and the second is the intrinsic dynamic range compression nonlinearity, which improves several metrics simultaneously without metric trade-off. Although the two beam coupling correlator was invented many years ago, its outstanding performance was recognized on only relatively simple images. There was no study about the performance of this correlator on complicated images and using different figures of merit. In this paper, the study is extended to more complicated images. For the first time, to our knowledge, we demonstrate simultaneous improvement in metrics performance without metric trade-off. The performance was evaluated compared to the classical joint transform correlator. A typical experimental result to validate the simulation results was also shown in this work. The best performing operation parameters were identified to guide the experimental work and for future comparison with other well-known optimal correlation filters.