Hyperspectral restoration employing low rank and 3D total variation regularization

This paper presents a novel mixed-noise removal method by employing low rank constraint and 3-D total variation regularization for hyperspectral image (HSI) restoration. The main idea of the proposed method is based on the assumption that the spectra in HSI lie in the same low rank subspace and both spatial and spectral domains exhibit the property of piecewise smoothness. The low rank property of HSI is exploited by the nuclear norm, while the spectral-spatial smoothness is explored by 3D total variation (3DTV) which is defined as a combination of 2-D spatial TV and 1-D spectral TV of the HSI cube. Finally, the proposed restoration model is effectively solved by alternating direction method of multipliers (ADMM). Experimental results on simulated HSI dataset validate the superior performance of the proposed method.

[1]  Jonathan Eckstein,et al.  Understanding the Convergence of the Alternating Direction Method of Multipliers: Theoretical and Computational Perspectives , 2015 .

[2]  Yi Chang,et al.  Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping , 2015, IEEE Transactions on Image Processing.

[3]  Liangpei Zhang,et al.  Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Liang Xiao,et al.  Supervised Spectral–Spatial Hyperspectral Image Classification With Weighted Markov Random Fields , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Yong Yu,et al.  Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Liangpei Zhang,et al.  Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Jing-Hao Xue,et al.  Denoising of Hyperspectral Images Using Group Low-Rank Representation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Liangpei Zhang,et al.  Hyperspectral Image Restoration Using Low-Rank Matrix Recovery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Emmanuel J. Candès,et al.  A Singular Value Thresholding Algorithm for Matrix Completion , 2008, SIAM J. Optim..

[10]  Zhihui Wei,et al.  Supervised hyperspectral image classification using sparse logistic regression and spatial-TV regularization , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[11]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[12]  Hongyan Zhang.  HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL , 2012 .

[13]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[14]  Minchao Ye,et al.  Hyperspectral Imagery Restoration Using Nonlocal Spectral-Spatial Structured Sparse Representation With Noise Estimation , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Jing-Hao Xue,et al.  Spectral Nonlocal Restoration of Hyperspectral Images With Low-Rank Property , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Yulong Wang,et al.  Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Liangpei Zhang,et al.  Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model , 2012, IEEE Transactions on Geoscience and Remote Sensing.