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• W2169576428 abstract "Whereas most previous works treating color or hyper- spectral image restoration use hybrid filters or data splitting, some new approaches consider multidimensional or tensor signal processing techniques. Tensor processing methods are based on multilinear algebra and are more efficient than 2-D filtering. However, they rely on orthogonal tensor flattening. The aim of this letter is to show that this orthogonal flattening may not be optimal for multidimensional images. We propose a method to adapt the flattening depending on the data set. Our proposed method is based on the estimation of main directions in multidimensional data. For this purpose, we extend the straight line detection algorithm. Multidimensional filtering method HOSVD - (K1,..., K <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>N</sub> ) is applied along the estimated directions. We also adapt a quadtree partitioning in order to split tensors into homogeneous sub-tensors to keep local characteristics. Considering some examples of color and hyperspectral images, we present some promising results." @default.
• W2169576428 created "2016-06-24" @default.
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• W2169576428 date "2008-01-01" @default.
• W2169576428 modified "2023-10-13" @default.
• W2169576428 title "Adaptive Flattening for Multidimensional Image Restoration" @default.
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• W2169576428 doi "https://doi.org/10.1109/lsp.2007.916045" @default.
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