Multiresolution models for random fields and their use in statistical image processing

Abstract

We describe a probabilistic framework for optimal multiresolution processing and analysis of spatial phenomena. Our developed multiresolution (MR) models are useful in describing random processes and fields. The scale recursive nature of the resulting models, leads to extremely efficient algorithms for optimal estimation and likelihood calculation. These models, which are described, have also provided a framework for data fusion, and produced new solutions to problems in computer vision (optical flow estimation), remote sensing (oceanography where dimensional complexity is in thousands), and various inverse problems of mathematical physics.