Super-Resolution Imaging
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Hierarchical Sampling with Constraints
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Image Resolution Enhancement with Hierarchical Hidden Fields
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Parallel Hidden Hierarchical Fields for Multi-scale Reconstruction
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Constrained sampling using simulated annealing
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Scientific image processing involves a variety of problems including image modelling, reconstruction, and synthesis. We are collaborating on an imaging problem in porous media, studied in-situ in an imaging MRI in which it is imperative to infer aspects of the porous sample at scales unresolved by the MRI. In this paper we develop an MCMC approach to resolution enhancement, where a low-resolution measurement is fused with a statistical model derived from a high-resolution image. Our approach is different from registration/super-resolution methods, in that the high and low resolution images are treated only as being governed by the same spatial statistics, rather than actually representing the same identical sample.