Abstract
Based on Markov Random Fields (MRF) theory, Bayesian methods have been accepted as an effective solution to overcome the ill-posed problems of image restoration and reconstruction. Traditionally, the knowledge in most of prior models is from a simply weighted differences between the pixel intensities within a small local neighborhood, so it can only provide limited prior information for regularization. Exploring the ways of incorporating more large-scale knowledge into prior model, this paper proposes an effective approach to incorporate large-scale image knowledge into MRF prior model. And a novel nonlocal prior is put forward. Relevant experiments in emission tomography prove that the proposed MRF nonlocal prior is capable of imposing more effective regularization on original reconstructions.
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Chen, Y. et al. (2007). A Novel Way of Incorporating Large-Scale Knowledge into MRF Prior Model. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_52
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DOI: https://doi.org/10.1007/978-3-540-73599-1_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
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