Bayesian imaging using Goodapos;s roughness measure-implementation on amassively parallel processor
Roysam, B.; Shrauner, J.A.; Miller, M.I.
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on
Volume , Issue , 11-14 Apr 1988 Page(s):932 - 935 vol.2
Digital Object Identifier   10.1109/ICASSP.1988.196742
Summary: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

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