177 / 2023-11-27 14:11:31
Research on Denoising Methods for Hyperspectral Images Based on Low-Rank Theory and Sparse Representation
Hyperspectral Images, Noise Estimation, Low-Rank, Sparse Representation
Final Paper
Wanning Tao / Tongji University
Na Liu / Tongji University
Yiming Chen / Tongji University
Jin Su / LanZhou University of Finance and Economics
Hui Xiao / Tongji University
Xuefeng Li / Tongji University
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.
Important Date
  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University
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