Abstract
This paper presents a new multi-way filtering method for multi-way images impaired by additive white noise. Instead of matrices or vectors, multidimensional images are considered as multi-way arrays also called tensors. Some noise removal techniques consist in vectorizing or matricizing multi-way data. That could lead to the loss of inter-bands relations. The presented filtering method consider multidimensional data as whole entities. Such a method is based on multilinear algebra. We adapt multi-way Wiener filtering to multidimensional images. Therefore, we introduce specific directions for tensor flattening. To this end, we extend the SLIDE algorithm to retrieve main directions of tensors, which are modeled as straight lines. To keep the local characteristics of images, we propose to adapt quadtree decomposition to tensors. Experiments on color images and on HYDICE hyperspectral images are presented to show the importance of flattening directions for noise removal in color images and hyperspectral images.
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Letexier, D., Bourennane, S., Blanc-Talon, J. (2007). Multidimensional Noise Removal Method Based on Best Flattening Directions. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_21
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DOI: https://doi.org/10.1007/978-3-540-74607-2_21
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