Adaptive Flattening for Multidimensional Image Restoration

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 filte...

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Veröffentlicht in:IEEE signal processing letters Jg. 15; S. 229 - 232
Hauptverfasser: Letexier, D., Bourennane, S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
Adaptive signal processing
Algebra
Color
Color image
Computer Science
Detection algorithms
Engineering Sciences
Filtering
Filters
higher order singular value decomposition (HOSVD)
hyperspectral image
image denoising
Image restoration
multidimensional filtering
Multidimensional signal processing
Multidimensional systems
Signal and Image processing
Tensile stress
tensor
ISSN:1070-9908, 1558-2361
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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 N ) 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.
AbstractList 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 N ) 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.
Author Letexier, D.
Bourennane, S.
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crossref_primary_10_1016_j_neuroimage_2017_04_017
crossref_primary_10_1109_TGRS_2012_2187063
crossref_primary_10_1109_TPAMI_2012_140
crossref_primary_10_1002_mrm_29817
crossref_primary_10_1007_s11042_020_09131_x
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SubjectTerms Adaptive signal processing
Algebra
Color
Color image
Computer Science
Detection algorithms
Engineering Sciences
Filtering
Filters
higher order singular value decomposition (HOSVD)
hyperspectral image
image denoising
Image restoration
multidimensional filtering
Multidimensional signal processing
Multidimensional systems
Signal and Image processing
Tensile stress
tensor
Title Adaptive Flattening for Multidimensional Image Restoration
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