Multiscale hidden Markov models for photon-limited imaging

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling and processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random held models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imaging applications involving Poisson statistics, and applications to image intensity analysis are examined.