Bayesian Comparison of Models for Images
Probabilistic models for images are analysed quantitatively using Bayesian hypothesis comparison on a set of image data sets. One motivation for this study is to produce models which can be used as better priors in image reconstruction problems.
The types of model vary from the simplest, where spatial correlations win the image are irrelevant, to more complicated ones based on a radial power law for the standard deviations of the coefficients produced by Fourier or Wavelet Transforms. In our experiments the Fourier model is the most successful, as its evidence is conclusively the highest. This ties in with the statistical scaling self-similarity (fractal property) of many images. We discuss the invariances of the models, and make suggestions for further investigations.
KeywordsFractal Property Wavelet Transform General Fractal Property Image Reconstruction Problem Uncorrelated Model
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