@article {281,
	title = {Statistical fusion of two-scale images of porous media},
	journal = {Advances in Water Resources},
	volume = {32},
	year = {2009},
	pages = {1567 - 1579},
	abstract = {<p>The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.</p>
},
	keywords = {Data fusion, magnetic resonance imaging, Porous media reconstruction, Posterior sampling, simulated annealing},
	issn = {0309-1708},
	doi = {http://dx.doi.org/10.1016/j.advwatres.2009.08.005},
	url = {http://www.sciencedirect.com/science/article/pii/S0309170809001274},
	author = {A Mohebi and P Fieguth and M A. Ioannidis}
}
