Efficient Stochastic Algorithms on Locally Bounded Image Space

Abstract

Stochastic relaxation algorithms in image processing are usually computationally intensive, partially because the images of interest comprise only a small fraction of the total (digital) configuration space. A new locally bounded image subspace is introduced, which is shown rich enough to contain most images which are reasonably smooth except for (possibly) sharp discontinuities. New versions of the Gibbs Sampler and Metropolis algorithms are defined on the locally bounded image space, and their asymptotic convergence is proven. Experiments in image restoration and reconstruction demonstrate that these algorithms perform more cost-effectively than the standard versions.