摘要详情
175 / 2023-11-26 21:16:21
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全文待审
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.
重要日期
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会议日期
11月02日
2023
至11月04日
2023
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12月15日 2023
初稿截稿日期
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12月20日 2023
注册截止日期
主办单位
IEEE Instrumentation and Measurement Society
Xidian University
Xidian University
联系方式
- Ms. 勤梅胡
- ma******@chytey.com
历届会议
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2025年11月21日 中国 Guangzhou
2025 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2023年11月02日 中国 Xi'an
2023 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2022年11月30日 中国 Harbin
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2021年10月21日 中国 Nanjing
2021 International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence -
2020年10月15日 中国 Xi'an
International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence
