Sliced sparsity measure for tensor to multispectral image denoising

Tongle Wu, Bin Gao*, W. L. Woo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

From the sparsity of vector to the sparsity of singular values, which essentially characterizes the low rank property of matrix. The sparsity measure based model is of significant interest in a range of contemporary applications in data analysis. However, there are different measurement strategies for sparse characterization of high dimensional tensor data. Albeit, most of the existing sparsity measures only consider the number of non-zero factor components, but ignore the geometric position distribution structure of non-zero elements in high-dimensional space. In this paper, based on the fact that sliced sparse distribution of the core tensor, a novel high order structure sparsity measure is proposed. More specifically, the sparsity measure unifies Tucker and CP tensor decomposition into a framework for general tensor. The CP decomposition of the core tensor with factor group sparse constraint realizes modeling the global low CP rank and the sliced sparse distribution of the non-zeros elements of the core tensor simultaneously. We apply minimizing high order structure sparse measurement to multispectral image denoising and deduce Alternating Direction Method of Multipliers (ADMM) optimization method to solve the model effectively. The subsequent experimental results show that the proposed algorithm is competitive with state-of-the art denoising methods.1

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing - Proceedings
Subtitle of host publicationICIP 2021
Place of PublicationPiscataway, US
PublisherIEEE
Pages3817-3821
Number of pages5
ISBN (Electronic)9781665441155
ISBN (Print)9781665431026
DOIs
Publication statusPublished - 19 Sept 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 19 Sept 202122 Sept 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
PublisherIEEE
Volume2021-September
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period19/09/2122/09/21

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