The outlier process [picture processing]

The authors discuss the problem of detecting outliers from a set of surface data. They start from the Bayes approach and the assumption that surfaces are piecewise smooth and corrupted by a combination of white Gaussian and salt and pepper noise. They show that such surfaces can be modelled by introducing an outlier process that is capable of `throwing away´ data. They make use of mean field techniques to finally obtain a deterministic network. The experimental results with real images support the model