Earticle

초록

영어

This paper presents some recent filtering methods based on the lower-rank tensor approximation approach for denoising tensor signals. In this approach, multicomponent data are represented by tensors, that is, multiway arrays, and the presented tensor filtering methods rely on multilinear algebra. First, the classical channel-by-channel SVD-based filtering method is overviewed. Then, an extension of the classical matrix filtering method is presented. It is based on the lower rank- K ,...,Kn  1 truncation of the HOSVD which performs a multimode Principal Component Analysis (PCA) and is implicitly developed for an additive white Gaussian noise. Two tensor filtering methods recently developed by the authors are also overviewed. The performances and comparative results between all these tensor filtering methods are presented for the cases of noise reduction in color images.

목차

Abstract
 1. Introduction
 2. Tensor representation and properties
 3. Tensor filtering problem formulation
  3.1. Channel-by-channel SVD-based filtering
  3.2 Tensor filtering based on multimode PCA
 4. Simulation results
  4.1. Performance criterion
  4.2 Denoising of color images
 5. Conclusion
 References

키워드

저자

• Salah Bourennane [ Ecole Centrale Marseille, Institut Fresnel-UMR ]
• Caroline Fossati [ Ecole Centrale Marseille, Institut Fresnel-UMR ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605