Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations

Lina Zhuang, Xiyou Fu*, Kwok Po Ng, Jose M. Bioucas-Dias

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

71 Citations (Scopus)


The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio of the measurements, thus calling for effective denoising techniques. HSIs from the real world lie in low-dimensional subspaces and are self-similar. The low dimensionality stems from the high correlation existing among the reflectance vectors, and self-similarity is common in real-world images. In this article, we exploit the above two properties. The low dimensionality is a global property that enables the denoising to be formulated just with respect to the subspace representation coefficients, thus greatly improving the denoising performance and reducing the computational complexity during processing. The self-similarity is exploited via a low-rank tensor factorization of nonlocal similar 3-D patches. The proposed factorization hinges on the optimal shrinkage/thresholding of the singular value decomposition (SVD) singular values of low-rank tensor unfoldings. As a result, the proposed method is user friendly and insensitive to its parameters. Its effectiveness is illustrated in a comparison with state-of-the-art competitors. A MATLAB demo of this work is available at https://github.com/LinaZhuang for the sake of reproducibility.

Original languageEnglish
Pages (from-to)10438-10454
Number of pages17
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number12
Early online date8 Jan 2021
Publication statusPublished - Dec 2021

Scopus Subject Areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

User-Defined Keywords

  • 3-D patches
  • hyperspectral image (HSI) denoising
  • low-rank tensor factorization
  • self-similarity


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