Abstract
Nonlinear digital filters based on the order statistic belong to the very powerful methods of image restoration. The well known is the median filter operating on scalar valued images. However, the operation of the median filter can be extended into multi-valued pixels, such as colour images. It appears that we can go even further and define such filters for tensor valued data. Such a median filter for tensor valued images was originally proposed by Welk et.al. [10]. In this paper we present a different approach to this concept: We propose the family of nonlinear order statistic filters operating on tensor valued data and provide the computational framework for their non-numerical implementation.
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Cyganek, B. (2006). Computational Framework for Family of Order Statistic Filters for Tensor Valued Data. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_15
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DOI: https://doi.org/10.1007/11867586_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44891-4
Online ISBN: 978-3-540-44893-8
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