Image reconstruction by adaptive bayesian classification with a locally proportional prior
Ruben Klein, S. James Press · Communication in Statistics- Theory and Methods · 1993
This paper concerns the problem of reconstructing images from noisy data by means of Bayesian classification methods. In Klein and Press, 1992, the authors presented a method for reconstructing images called Adaptive Bayesian Classification (ABC). The ABC procedure was shown to preform very well in simulation experiments. The ABC procedure was multistaged; moreover, it involved selecting a prior at Stage n that was the posterior at Stage n - 1. In this paper the authors show that we can improve upon ABC for some problems by modifying the way we take the prior at each stage. The new proposal is to take the prior for the pixel label at each stage as proportional to the number of pixels with that label in a small neighborhood of the pixel. The ABC procedure with a locally proportional prior (ABC/LPP) tends to improve upon the ABC procedure for some problems because the prior in the iterative portion of ABC/LPP is contextual, while that in ABC in non- contextual.