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A Graph-Regularized Non-local Hyperspectral Image Denoising Method

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Geometry and Vision (ISGV 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1386))

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Abstract

A lot of hyperspectral images (HSIs) are corrupted by noises when they are captured. Noise removal is an essential pre-processing for the noisy HSIs. Though denoising algorithms for common (grayscale or RGB) images have been studied for decades, HSIs have their inherent characteristics, so denoising algorithms for HSIs need to be specially designed. In this work, we have developed a non-local denoising algorithm for HSIs based on multi-task graph-regularized sparse nonnegative matrix factorization (MTGSNMF). MTGSNMF delivers noise removal in both spatial and spectral views. In spatial view, patch-based sparse recovery is performed by sparse nonnegative matrix factorization (SNMF), which conducts noise suppression and local pattern preservation. Graph regularization is imposed on the SNMF model for maintaining the non-local similarities between patches. In spectral view, spectral structure is extracted by multi-task learning, i.e., denoising tasks of different bands are bound by sharing the same coefficient matrix. By exploiting the non-local similarity in spatial view and spectral structure in spectral view, MTGSNMF achieves superior denoising performance on HSI datasets.

Supported partly by the National Natural Science Foundation of China (grant numbers 61701468 and 62071421), and partly by the National Key Research and Development Program of China (grant number 2018YFB0505000).

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Notes

  1. 1.

    The code of BM4D is available at https://www.cs.tut.fi/~foi/GCF-BM3D/BM4D_v3p2.zip.

  2. 2.

    The code of NLMF is available at https://www.mathworks.com/matlabcentral/fileexchange/27395-fast-non-local-means-1d-2d-color-and-3d.

  3. 3.

    The code of NGmeet is available at https://github.com/quanmingyao/NGMeet.

  4. 4.

    The code of the proposed MTGSNMF is available at https://github.com/yeminchao/MTGSNMF.

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Authors and Affiliations

  1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou, 310018, China

    Ling Lei, Binqian Huang, Minchao Ye & Hong Chen

  2. College of Computer Science, Zhejiang University, Hangzhou, 310027, China

    Yuntao Qian

Authors
  1. Ling Lei
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  2. Binqian Huang
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  3. Minchao Ye
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  4. Hong Chen
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  5. Yuntao Qian
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Corresponding author

Correspondence to Minchao Ye .

Editor information

Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  2. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  3. Auckland Bioengineering House, Auckland, New Zealand

    Harvey Ho

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Lei, L., Huang, B., Ye, M., Chen, H., Qian, Y. (2021). A Graph-Regularized Non-local Hyperspectral Image Denoising Method. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_25

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  • DOI: https://doi.org/10.1007/978-3-030-72073-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72072-8

  • Online ISBN: 978-3-030-72073-5

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