Research on Denoising Methods for Hyperspectral Images Based on Low-Rank Theory and Sparse Representation

doi:10.1109/ICSMD60522.2023.10490870

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	Research on Denoising Methods for Hyperspectral Images Based on Low-Rank Theory and Sparse Representation
	Tao, Wanning 1; Liu, Na 1; Chen, Yiming1 ; Su, Jin 2; Xiao, Hui 1; Li, Xuefena 3
	2023
会议名称	2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023
会议录名称	ICSMD 2023 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings
会议日期	November 2, 2023 - November 4, 2023
会议地点	Xi'an, China
出版者	Institute of Electrical and Electronics Engineers Inc.
摘要	This study addresses the issue of noise interference in hyperspectral images (HSI). By combining singular value decomposition (SVD) with an adaptive block algorithm, an improved algorithm for estimating noise intensity is proposed, aiming for precise assessment of noise levels. Additionally, an enhanced denoising method for hyperspectral images is introduced by integrating low-rank theory and sparse representation algorithms. The research results indicate that, for the Indian Pines public dataset, the denoising performance of the study surpasses existing algorithms by over 3.0 dB. Furthermore, robustness in estimating noise intensity is observed. Valuable insights for denoising similarly structured data with low signal-to-noise ratios are provided by this research, contributing meaningfully to the field. © 2023 IEEE.
关键词	Image denoising Image enhancement Signal to noise ratio De-noising Denoising methods HyperSpectral Hyperspectral image Image-based Low-rank Noise estimation Noise intensities Sparse representation Theory representations
DOI	10.1109/ICSMD60522.2023.10490870
收录类别	EI
语种	英语
EI入藏号	20241816008363
EI主题词	Singular value decomposition
EI分类号	716.1 Information Theory and Signal Processing ; 723.2 Data Processing and Image Processing ; 921 Mathematics
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/36176
专题	会计学院
通讯作者	Liu, Na
作者单位	1.Tongji University, College of Electronic and Information Engineering, Shanghai, China; 2.Lanzhou University of Finance and Economics, College of Information Engineering, Lanzhou, China; 3.Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, College of Electronic and Information Engineering, Shanghai, China
推荐引用方式 GB/T 7714	Tao, Wanning,Liu, Na,Chen, Yiming,et al. Research on Denoising Methods for Hyperspectral Images Based on Low-Rank Theory and Sparse Representation[C]:Institute of Electrical and Electronics Engineers Inc.,2023.