Unsupervised segmentation of piecewise constant images from incomplete, distorted and noisy data

摘要

The paper tackles the problem of piecewise constant image segmentation. A triple degradation model is assumed for the observation system: missing data, non-linear gain and additive noise. The proposed solution follows a Bayesian strategy that yields optimal decisions and estimations. A numerical approach is used to explore the intricate posterior distribution: a Gibbs sampler including a Metropolis-Hastings step. The posterior samples are subsequently used in computing the estimates and the decisions. A first numerical evaluation provided encouraging results despite the triple degradation.