Bayesian imaging using Good's roughness measure-implementation on a massively parallel processor
- 6 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15206149,p. 932-935
- https://doi.org/10.1109/icassp.1988.196742
Abstract
A constrained maximum-likelihood estimator is derived by incorporating a rotationally invariant roughness penalty proposed by I.J. Good (1981) into the likelihood functional. This leads to a set of nonlinear differential equations the solution of which is a spline-smoothing of the data. The nonlinear partial differential equations are mapped onto a grid via finite differences, and it is shown that the resulting computations possess a high degree of parallelism as well as locality in the data-passage, which allows an efficient implementation on a 48-by-48 mesh-connected array of NCR GAPP processors. The smooth reconstruction of the intensity functions of Poisson point processes is demonstrated in two dimensions.<>Keywords
This publication has 5 references indexed in Scilit:
- The role of likelihood and entropy in incomplete-data problems: Applications to estimating point-process intensities and toeplitz constrained covariancesProceedings of the IEEE, 1987
- The Use of Sieves to Stabilize Images Produced with the EM Algorithm for Emission TomographyIEEE Transactions on Nuclear Science, 1985
- C87. Roughness penalties, invariant under rotation for multidimensional probability density estimationJournal of Statistical Computation and Simulation, 1981
- Nonparametric Maximum Likelihood Estimation of Probability Densities by Penalty Function MethodsThe Annals of Statistics, 1975
- Non-parametric Roughness Penalty for Probability DensitiesNature Physical Science, 1971