Abstract
This paper presents a new algorithm for the restoration and reconstruction of images. A linear predictor provides the regularization function. An adaptive version of the algorithm is developed by matching a weighting function to the previously selected regularization function. The adaptive regularization simultaneously leads to proper noise suppression and enhanced resolution of discontinuities.
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References
H.C. Andrews, B.R. Hunt: Digital Image Restoration, Prentice-Hall, Englewood Cliffs, N.J., 1977
B. Bundschuh: Adaptive Image Restoration and Reconstruction, Third International Seminar on Digital Image Processing in Medicine, Remote Sensing and Visualization of Information, Riga, Latvia, April 1992
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© 1993 Springer-Verlag Berlin Heidelberg
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Bundschuh, B. (1993). A linear predictor as a regularization function in adaptive image restoration and reconstruction. In: Chetverikov, D., Kropatsch, W.G. (eds) Computer Analysis of Images and Patterns. CAIP 1993. Lecture Notes in Computer Science, vol 719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57233-3_15
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DOI: https://doi.org/10.1007/3-540-57233-3_15
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57233-6
Online ISBN: 978-3-540-47980-2
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