Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 303-307.doi: 10.11896/jsjkx.210200103

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Hyperspectral Image Denoising Based on Robust Low Rank Tensor Restoration

WU Yong1,2, LIU Yong-jian1, TANG Tang2, WANG Hong-lin2, ZHENG Jian-cheng2   

  1. 1 School of Computer Science and Technology,Wuhan University of Technology,Wuhan 430010,China
    2 Radar Sergeant School of Air force Early Warning Academy,Wuhan 430010,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:WU Yong,born in 1982,postgraduate,master candidate,intermediate title.His main research interests include image processing and object recognition,etc.
  • Supported by:
    National Natural Science Foundation of China(61401504).

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Abstract: Denoising is an important preprocessing step to further analyze the hyperspectral image (HSI),and many denoising methods have been used for the denoising of the HSI data cube.However,the traditional denoising methods are sensitive to outliers and non-Gaussian noise.In this paper,by making using of the low-rank tensor property of the clean HSI data and the sparsity property of the outliers and non-Gaussian noise,we propose a new model based on the robust low-rank tensor recovery,which can retain the global structure of HSI and clean the outliers and mixed noise.The proposed model can be solved by the inexact augmented Lagrangian method.Experiments on simulated and real hyperspectral data show that the proposed algorithm is efficient for HSI restoration.

Key words: Gaussian noise, HSI denoising, Hyperspectral image denoising, Impulsive noise, Low-rank tensor

CLC Number: 

  • TP751
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