Markov random field-based image subsampling method

Abstract

Abstract The use of Bayesian models for the reconstruction of images degraded by both some blurring function H and the presence of noise has become popular in recent years. Making an analogy between classical degradation processes and resampling, we propose a Bayesian model for generating finer resolution images. The approach involves defining resampling, or aggregation, as a linear operator applied to an original picture to produce derived lower resolution data which represent our available experimental infor-mation. Within this framework, the operation of making inference on the orginal data can be viewed as an inverse linear transformation problem. This problem, formalized through Bayes' theorem, can be solved by the classical maximum a posteriori estimation procedure. Image local characteristics are assumed to follow a Gaussian Markov random field. Under some mild assumptions, simple, iterative and local operations are involved, making parallel 'relaxation' processing feasible. experimental results are shown on some images, for which good subsampling estimates are obtained.