Bayesian Image Processing in Two Dimensions

Abstract

A Bayesian image processing (BIP) formalism which incorporates a priori amplitude and spatial probability density information was applied to two-dimensional source fields. For valid, moderately restrictive a priori information, strikingly improved results for ideal and experimental radioisotope phantom imaging data, compared to a standard non-Bayesian formalism (maximum likelihood, ML), were obtained. The applicability of a fast Fourier transform technique for "convolution" calculations, a reduced-region restriction for the initial "deconvolution" calculations, and a relaxation parameter for accelerating convergence are considered.