Maximum-entropy techniques, which have been successfully used to reconstruct a function
based on knowledge of a limited number of linear functional values, are extended to a family …

A general approach to the problem of reconstructing a non-negative function from finitely
many linear functional values is to select from among all data consistent functions that one …

The minimum distance approach for reconstructing a positive function based on knowledge
of finitely many linear functional values is examined. Two important classes of directed …

Image reconstruction from projections can be viewed as a linear inverse problem with
discrete data if the number of projections is small. The input data corresponds to integral …

Two-dimensional digital image reconstruction is an important imaging process in many of
the physical sciences. If the data are insufficient to specify a unique reconstruction, an …

We examine here the problem of reconstructing an X‐ray attenuation function from
measurements of its integrals. The approach that is taken is to maximize the difference of the …

Maximum entropy methods have proven to be a powerful tool for reconstructing data from
incomplete measurements or in the presence of noise. In this note, we apply the method to …

Entropy has been widely used to solve inverse problems, especially when a complete set of
data is not available to obtain a unique solution to the problem. In this paper we show that a …

Minimum distance approaches are considered for the reconstruction of a real function from
finitely many linear functional values. An optimal class of distances satisfying an …

Abstract A Maximum Aposteriori Probability (MAP-) approach is considered for
reconstructing an image from noisy projections. This approach needs, according to Bayes …