research-article

Multispectral Image Denoising via Structural Tensor Sparsity Promoting Model

Authors:

Qing ZhuAuthors Info & Claims

MMAsia '22: Proceedings of the 4th ACM International Conference on Multimedia in Asia

Article No.: 19, Pages 1 - 7

https://doi.org/10.1145/3551626.3564948

Published: 13 December 2022 Publication History

Abstract

Multispectral images (MSIs) contain more spectral information than traditional 2D images, which can provide a more accurate representation of objects. MSIs are easily affected by various noises when captured by sensors. In recent years, many MSI denoising methods, especially the Kronecker-basis-representation (KBR) method, have achieved great success. KBR uses tensor representation and decomposition to achieve good MSI denoising performance. However, each full band patch (FBP) group is decomposed in this method so that too many dictionary atoms are generated. In this paper, we propose a structural tensor sparsity promoting (STSP) model for MSI denoising. In order to decrease the number of dictionary atoms, we cluster FBP groups and learn orthogonal dictionaries for each class rather than each FBP group. To improve the denoising performance, the structural similarity among FBP groups are utilized in the STSP model by enforcing nonlocal centralized sparse constraint, where the compromise parameter is statistically and adaptively determined. Experimental results on the the CAVE dataset demonstrate that our model outperforms the state-of-art methods in terms of both objective and subjective quality.

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Cited By

Barbu T(2023)Vector-valued Fourth-order Reaction-diffusion based Photon-limited Multi-channel Image Filtering2023 International Symposium on Signals, Circuits and Systems (ISSCS)10.1109/ISSCS58449.2023.10190950(1-4)Online publication date: 13-Jul-2023
https://doi.org/10.1109/ISSCS58449.2023.10190950

Index Terms

Multispectral Image Denoising via Structural Tensor Sparsity Promoting Model
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

MMAsia '22: Proceedings of the 4th ACM International Conference on Multimedia in Asia

December 2022

296 pages

ISBN:9781450394789

DOI:10.1145/3551626

Conference Chair:
Shuqiang Jiang
CASROLE@GENERAL CHAIR
,
General Chairs:
Kiyoharu Aizawa
The University of Tokyo
,
Phoebe Chen
La Trobe
,
Keiji Yanai
The University of Electro-Communications

Copyright © 2022 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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New York, NY, United States

Publication History

Published: 13 December 2022

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Scientific Research Common Program of Beijing Municipal Commission of Education
National Natural Science Foundation of China under Grant
International Research Cooperation Seed Fund of Beijing University of Technology

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MMAsia '22

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SIGMM

MMAsia '22: ACM Multimedia Asia

December 13 - 16, 2022

Tokyo, Japan

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Overall Acceptance Rate 59 of 204 submissions, 29%

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Cited By

Barbu T(2023)Vector-valued Fourth-order Reaction-diffusion based Photon-limited Multi-channel Image Filtering2023 International Symposium on Signals, Circuits and Systems (ISSCS)10.1109/ISSCS58449.2023.10190950(1-4)Online publication date: 13-Jul-2023
https://doi.org/10.1109/ISSCS58449.2023.10190950

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