Maximum entropy image reconstruction

Image reconstruction by conditional entropy maximisation for PET system - Vision, Image and Signal Processing, IEE Proceedings-

The authors show that the conditional entropy maximisation algorithm is a generalised version of the maximum likelihood algorithm for positron emission tomography (PET). Promising properties of the conditional entropy maximisation algorithm are as follows: an assumption is made that the entropy of the information content of the data should be maximised; it is a consistent way of selecting an im...

Bayesian Entropy Approach with the Maximum Entropy Principle in Image Reconstruction from Microwave Scattered Field Data

Bayesian Maximum Entropy Image Reconstruction

This paper presents a Bayesian interpretation of maximum entropy image reconstruction and shows that exp(αS(/, m)), where S(f,m) is the entropy of image / relative to model m, is the only consistent prior probability distribution for positive, additive images. It also leads to a natural choice for the regularizing parameter α, that supersedes the traditional practice of setting χ = N. The new c...

Maximum Entropy Formalism and Genetic Algorithms

The maximum entropy principle [1 – 3] is a powerful tool in the investigations of image reconstruction, spectral analysis, seismic inversion, inverse scattering etc. It is proven to be the only consistent method for inferring from incomplete information. Here we show that the maximum entropy principle can be cast into a unconstrained optimization problem and therefore genetic algorithms [4, 5] ...

The Image Restoration/Reconstruction Problem

This lecture reviews the image restoration/reconstruction problem in its general setting. We ̄rst discuss linear methods for solving the problem of image deconvolution, i.e. the case in which the data is a convolution of a point-spread function and an underlying unblurred image. Next, non-linear methods are introduced in the context of Bayesian estimation, including MaximumLikelihood and Maximum...

Maximum entropy based non-negative optoacoustic tomographic image reconstruction

Optoacoustic (photoacoustic) tomography reconstructs maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model employed, noise and other experimental factors, the images often contain errors, occasionally manifested as negative values. We present optoacoustic tomography based on an entropy maximization ...