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).

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
[1]ZHAO Y Q,YANG J X.Hyperspectral Image Denoising viaSparse Representation and Low-Rank Constraint[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(1):296-308.
[2]WANG Z P,TYO J S,HAYAT M M.Generalized Signal-to-Noise Ratio for Spectral Sensors with Correlated Bands[J].Journal of The Optical Society of America A,2008,25(10):2528-2534.
[3]ZHANG L F,ZHANG L P,TAO D C,et al.Compression of Hyperspectral Remote Sensing Images by Tensor Approach[J].Neurocomputing,2015,147:358-363.
[4]ZHANG H Y,HE W,ZHANGL P,et al.Hyperspectral Image Restoration Using Low-Rank Matrix Recovery[J].IEEE transactions on geoscience and remote sensing,2013,52(8):4729-4743.
[5]GUO X,HUANG X,ZHANG L P,et al.Hyperspectral Image Noise Reduction Based on Rank-1 Tensor Decomposition[J].ISPRS Journal of Photogrammetry and Remote Sensing,2013,83:50-63.
[6]MA J Y,ZHAO J,TIANJ W,et al.Robust Point Matching via Vector Field Consensus[J].IEEE Transactions on Image Processing.2014,23(4):1706-1721.
[7]YUAN Q Q,ZHANG L P,SHEN H F.Hyperspectral Image Denoising with A Spatial-Spectral View Fusion Strategy[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(5):2314-2325.
[8]LIN T,BOURENNANE S.Survey of Hyperspectral Image Denoising Methods Based on Tensor Decompositions[J].EURASIP journal on Advances in Signal Processing,2013,186(1):1-11.
[9]MUTI D,BOURENNANE S,MAROT J.Lower-Rank Tensor Approximation and Multiway Filtering[J].SIAM Journal on Matrix Analysis and Applications,2008,30(3):1172-1204.
[10]RENARD N,BOURENNANE S,BLANC-TALON J.Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images[J].IEEE Geoscience and Remote Sensing Letters,2008,5(2):138-142.
[11]LIU X F,BOURENNANE S,FOSSATI C.Denoising of Hyperspectral Images Using the PARAFAC Model and Statistical Performance Analysis[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(10):3717-3724.
[12]LIN T,BOURENNANE S.Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(6):3529-3541.
[13]LI Q,LI H Q,LU Z B,et al.Denoising of Hyperspectral Images Employing Two-Phase Matrix Decomposition[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(9):3742-3754.
[14]GOLDFARB D,QIN Z W.Robust Low-Rank Tensor Recovery:Models and Algorithms[J].SIAM Journal on Matrix Analysis and Applications,2014,35(1):225-253.
[15]WRIGHT J,GANESH A,RAO S,et al.Robust Principal Component Analysis:Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization[R].Coordinated Science Laboratory Report no.UILU-ENG-09-2210,DC-243,2009.
[16]GANDY S,RECHT B,YAMADA I.Tensor Completion andLow-n-Rank Tensor Recovery via Convex Optimization[J].Inverse Problems,2011,27(2):1-19.
[17]LIU G,LIN Z,YAN S,et al.Robust Recovery of SubspaceStructures by Low-Rank Representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(1):171-184.
[18]LIN Z C,CHEN M M,WU L,et al.The Augmented Lagrange Multiplier Method for Exact recovery of Corrupted Low-Rank Matrices[R].UIUC Tech.Rep.UILU-ENG-09-2215 (University Illinois at Urbana Champaign,Champaign,Illinois,2009).
[19]ZHANG Y.Recent advances in alternating direction methods:practice and theory[R].Presented at IPAM Workshop:Numerical Methods for Continuous Optimization.Los Angeles,California,2010.
[20]ZHANG L,ZHANG L,MOU X Q,et al.FSIM:A Feature Similarity Index for Image Quality Assessment[J].IEEE Transactions on Image Processing,2011,20(8):2378-2386.
[21]DABOV K,FOI A,EGIAZARIAN K.Video Denoising bySparse 3D Transform-Domain Collaborative Filtering[C]//Proceedings of 15th European Signal Processing Conference.Pozna'n,Poland,2007.
[1] ZHENG Jian-wei, HUANG Juan-juan, QIN Meng-jie, XU Hong-hui, LIU Zhi. Hyperspectral Image Denoising Based on Non-local Similarity and Weighted-truncated NuclearNorm [J]. Computer Science, 2021, 48(9): 160-167.
[2] LIN Yun, HUANG Zhen-hang, GAO Fan. Diffusion Variable Tap-length Maximum Correntropy Criterion Algorithm [J]. Computer Science, 2021, 48(5): 263-269.
[3] LIN Yun, HUANG Zhen-hang, GAO Fan. Diffusion Maximum Correntropy Criterion Variable Step-size Affine Projection Sign Algorithm [J]. Computer Science, 2020, 47(6): 242-246.
[4] DONG Qing, LIN Yun. Kernel Fractional Lower Power Adaptive Filtering Algorithm Against Impulsive Noise [J]. Computer Science, 2019, 46(11A): 80-82.
[5] ZHOU Xue-qian, WU Xiao-fu and YU Xun-jian. LDPC Coded Primary Transmitter Authentication Schemes in Cognitive Radio Networks [J]. Computer Science, 2017, 44(7): 89-93.
Full text



No Suggested Reading articles found!