Non-stationary t-distribution prior for image source separation from blurred observations
Kayabol K., Kuruoglu E. E.
Physical Sciences and Engineering. Astronomy Probability and Statistics. Markov processes Student-t distribution Probability and Statistics. Probabilistic algorithms (including Monte Carlo) Markov random fields
We propose a non-stationary spatial image model for blind image separation problem. Our model is defined on first order image differentials. We model the image differentials using t-distribution with space varying scale parameters. This prior image model has been used in the Bayesian formulation and the image source are estimated using a Langevin sampler method. We have tested the proposed model on astrophysical image mixtures and obtained better results regarding to stationary model.
Source: LVA/ICA 2010 - Latent Variable Analysis and Signal Separation. 9th International Conference, pp. 506–513, St. Malo, France, 27-30 September 2010
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@inproceedings{oai:it.cnr:prodotti:44392,
title = {Non-stationary t-distribution prior for image source separation from blurred observations},
author = {Kayabol K. and Kuruoglu E. E.},
doi = {10.1007/978-3-642-15995-4_63},
booktitle = {LVA/ICA 2010 - Latent Variable Analysis and Signal Separation. 9th International Conference, pp. 506–513, St. Malo, France, 27-30 September 2010},
year = {2010}
}
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