The optimal correlation filter for the discrimination or classification of multiclass stochastic images buried in additive noise was designed. We consider noise in images as the (K + 1)th class of stochastic images, so the K class with noise problem becomes a problem of (K + 1) classes: K class without noise plus the (K + 1)th class of noise. Experimental verifications with both low-frequency background noise and high-frequency shot noise show that the new filter design is reliable.
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