A mathematical analysis of the distortion tolerance in correlation filters is presented. A good measure for distortion performance is shown to be a generalization of the minimum average correlation energy criterion. To optimize the filter's performance, we remove the usual hard constraints on the outputs in the synthetic discriminant function formulation. The resulting filters exhibit superior distortion tolerance while retaining the attractive features of their predecessors such as the minimum average correlation energy filter and the minimum variance synthetic discriminant function filter. The proposed theory also unifies several existing approaches and examines the relationship between different formulations. The proposed filter design algorithm requires only simple statistical parameters and the inversion of diagonal matrices, which makes it attractive from a computational standpoint. Several properties of these filters are discussed with illustrative examples.
A general algorithm for synthesizing purely real correlation filters in the frequency domain is developed by using the method of Lagrange multipliers. This method can be applied to filters that are derived by using linearly constrained quadratic minimization. The synthesis of purely real versions of minimum average correlation energy filters, minimum-variance synthetic discriminant functions, and other synthetic-discriminant-function-type filters is discussed to illustrate this approach. Their performance is found to be somewhat less than that of the original complex filters but still adequate for practical applications. The main advantage of this approach is that optimum purely real filters can be generated that are easy to implement in spatial light modulators without holograms and that yield the correlation output on the zero-order beam.
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