Abstract
This contribution presents a variation of the Wiener filter criterion, i.e. minimizing the mean squared error, by combining it with the main principle of normalized convolution, i.e. the introduction of prior information in the filter process via the certainty map. Thus, we are able to optimize a filter according to the signal and noise characteristics while preserving edges in images. In spite of its low computational costs the proposed filter schemes outperforms state of the art filter methods working also in the spatial domain. Furthermore, the Wiener filter paradigm is extended from scalar valued data to tensor valued data.
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Krajsek, K., Mester, R. (2006). The Edge Preserving Wiener Filter for Scalar and Tensor Valued Images. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_10
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DOI: https://doi.org/10.1007/11861898_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
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