Abstract
In this paper we propose a Bayesian approach with Maximum Entropy (ME) priors to solve an integral equation which arises in various image restoration and reconstruction problems. Our contributions in this paper are the following: i) We discuss the a priori probability distributions which are deduced from different a priori constraints when the principle of ME is used. ii) When the a priori knowledge is only the noise covariance matrix and the image total intensity, and when the maximum a posteriori (MAP) is chosen as the decision rule to determine the values of image pixels, we show that the solution may be obtained by minimizing a criterion in which the structural entropy of the image is used as a particular choice of a regularization functional. The discussion is illustrated with some simulated results.
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© 1989 Springer Science+Business Media Dordrecht
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Mohammad-Djafari, A., Demoment, G. (1989). Maximum Entropy and Bayesian Approach in Tomographic Image Reconstruction and Restoration. In: Skilling, J. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7860-8_18
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DOI: https://doi.org/10.1007/978-94-015-7860-8_18
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4044-2
Online ISBN: 978-94-015-7860-8
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