Classification of multiclassed stochastic images buried in additive noise
Zu-Han Gu, Sing H. Lee · Journal of the Optical Society of America A · 1987
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.