We make an analogy between images and statistical mechanics systems. Pixel gray levels
and the presence and orientation of edges are viewed as states of atoms or molecules in a …

We make an analogy between images and statistical mechanics systems. Pixel gray levels
and the presence and orientation of edges are viewed as states of atoms or molecules in a …

There has been much recent interest in Bayesian image analysis, including such topics as
removal of blur and noise, detection of object boundaries, classification of textures, and …

The parameters of the prior, the hyperparameters, play an important role in Bayesian image
estimation. Of particular importance for the case of Gibbs priors is the global …

The Bayesian method for restoring an image corrupted by added Gaussian noise uses a
Gibbs prior for the unknown clean image. The potential of this Gibbs prior penalizes …

We develop the statistical mechanics formulation of the image restoration problem,
pioneered by Geman and Geman (1984). Using Bayesian methods we establish the …

Methods are reviewed for choosing regularized restorations in image processing. In
particular, a method developed by Galatsanos and Katsaggelos (see ibid., vol. 1, p. 322 …

The basic frameworks and techniques of the Bayesian approach to image restoration are
reviewed from the statistical-mechanical point of view. First, a few basic notions in digital …

This thesis addresses a number of issues concerned with the restoration of one type of
image sequence, namely archived black and white motion pictures. These are often a …

The authors propose a Bayesian model for the restoration of images based on counts of
emitted photons. The model treats blurring within the context of an incomplete data problem …