Estimation of noncausal stochastic model for a random image by means of 'whiteness'
Hisanao Ogura, Shigeyuki Miyagi, N. Takahashi · 1992
The authors propose a new, simple method for estimating a noncausal filter using the concept of the 'whiteness', and obtain the noncausal model parameters by minimizing the 'whiteness' of the output of a spatial filter. The method is successfully applied to several simulated images and practical textures, thus demonstrating its utility. The likelihood functional for a noncausal model, on the other hand, takes on a complicated form, and its maximization involves a troublesome computation when compared to the simple method using 'whiteness'. Determination of model size by means of the 'whiteness' is also shown to be more effective than AIC or BIC in the case of image model estimation.>