Paper Info
Title
Main flattening directions and Quadtree decomposition for multi-way Wiener filtering
Abstract
Previous studies have shown that multi-way Wiener filtering improves the restoration of tensors impaired by an additive white Gaussian noise. Multi-way Wiener filtering is based on the distinction between noise and signal subspaces. In this paper, we show that the lower is the signal subspace dimension, the better is the restored tensor. To reduce the signal subspace dimension, we propose a method based on array processing technique to estimate main orientations in a flattened tensor. The rotation of a tensor of its main orientation values permits to concentrate the information along either rows or columns of the flattened tensor. We show that multi-way Wiener filtering performed on the rotated noisy tensor enables an improved recovery of signal tensor. Moreover, we propose in this paper a quadtree decomposition to avoid a blurry effect in the recovered tensor by multi-way Wiener filtering. We show that this proposed block processing reduces the whole blur and restores local characteristics of the signal tensor. Thus, we show that multi-way Wiener filtering is significantly improved thanks to rotations of the estimated main orientations of tensors and a block processing approach.
Year
DOI
Venue
2007
10.1007/s11760-007-0022-7
Signal, Image and Video Processing
Keywords
Field
DocType
multi-way wiener filtering · svd based tensor filtering · tensor · flattening matrix · orientation estimation · quadtree decomposition,wiener filter,additive white gaussian noise
Array processing,Flattening,Tensor,Artificial intelligence,Geometry,Wiener filter,Pattern recognition,Wiener deconvolution,Algorithm,Linear subspace,Signal subspace,Additive white Gaussian noise,Mathematics
Journal
Volume
Issue
ISSN
1
3
1863-1711
Citations 
PageRank 
References 
3
0.40
11
Authors
3
Name
Order
Citations
PageRank
Damien Letexier1293.10
Salah Bourennane295982.70
Jacques Blanc-Talon378050.64