Abstract
Many medical imaging systems produce images that are degraded by statistical noise and blurring. This paper describes a physical model for the generation of images associated with these systems and presents a restoration algorithm that is designed to compensate for these sources of degradation. The restoration algorithm is based on the idea of Bayesian data augmentation and utilizes a Gibbs prior for the image. This prior incorporates two essential features necessary for the restoration of such images. First, boundary detection is included so that nonhomogeneous regions can be identified. Second, an expanded neighborhood system is proposed to permit the deconvolution of blurring effects. Additionally, a procedure for choosing parameters of the Gibbs prior is discussed.
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© 1992 Springer-Verlag Berlin Heidelberg
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Johnson, V.E., Wong, W.H., Hu, X., Chen, CT. (1992). Data Augmentation Schemes Applied to Image Restoration. In: Todd-Pokropek, A.E., Viergever, M.A. (eds) Medical Images: Formation, Handling and Evaluation. NATO ASI Series, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77888-9_14
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DOI: https://doi.org/10.1007/978-3-642-77888-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-77890-2
Online ISBN: 978-3-642-77888-9
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