Optical EngineeringInput image spectral density estimation for real-time adaption of correlation filters
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The problem of image noise estimation for improved noise robustness and discrimination capabilities of optical correlation filters is discussed. Colored noise is often used in the literature as an approximation to the true noise spectral density in the input image of a correlator. This conjecture is verified on different kinds of input images, i.e., their power spectral densities are fitted to a colored noise model. The quality of the resulting approximation is discussed. We then show that incorporating this noise estimation into optimal trade-off filters can significantly improve both the discrimination capabilities and the SNR of the resulting adaptive correlation filter above that of the classical filters for which the noise parameters are not estimated. Although its performance is in general found to be markedly inferior to that of true nonlinear filtering techniques that are optimal for adaptive image correlation, the proposed adaptive method is attractive in terms of computation time. Possible optical implementations of the proposed method are also discussed.