Hyperspectral image restoration based on color superpixel segmentation
Abstract
Hyperspectral images (HSI) are often degraded by various types of noise during the acquisition process, such as Gaussian noise, impulse noise, dead lines and stripes, etc. Recently, there exists a growing attenrion on low-rank matrix/tensor-based methods for HSI data restoration, assuming that the overall data is low-rank. However, the assumption of overall low-rankness often proves inaccurate due to the spatially heterogeneous local similarity characteristics of HSI. Traditional cube-based methods involve dividing the HSI into fixed-size cubes. However, using fixed-size cubes does not provide flexible coverage of locally similar regions at varying scales. Inspired by superpixel segmentation, this paper proposes the Shrink Low-rank Super-tensor (SLRST) approach for HSI recovery. Instead of using fixed-size cubes, SLRST employs a size-adaptive super-tensor. The proposed approach is effectively solved using the Alternating Direction Method of Multipliers (ADMM). Numerical experiments on HSI data verify that the proposed method outperforms other competing methods.References
H. Zhang, J. Li, Y. Huang, and L. Zhang, A nonlocal weighted joint sparse representation classification method for hyperspectral imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2056-2065, 2014.
W. He, H. Zhang and L. Zhang, Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3909-3921, 2017.
N. M. Nasrabadi, Hyperspectral target detection : An overview of current and future challenges, IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 34-44, 2014.
F. Luo, L. Zhang, B. Du and L. Zhang, Dimensionality reduction with enhanced hybrid-graph discriminant learning for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 8, pp. 5336-5353, 2020.
L. -J. Deng, G. Vivone, W. Guo, M. Dalla Mura and J. Chanussot, A variational pansharpening approach based on reproducible kernel hilbert space and heaviside function, IEEE Transactions on Image Processing, vol. 27, no. 9, pp. 4330-4344, 2018
X. Liu, S. Bourennane and C. Fossati, Denoising of hyperspectral images using the PARAFAC model and statistical performance analysis, IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3717-3724, 2012,.
G. Martin, J. M. Bioucas-Dias and A. Plaza, B-HYCA: Blind hyperspectral compressive sensing, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, pp. 2856-2859, 2015.
M. Yang, Q. Luo, W. Li, M. Xiao, Nonconvex 3D array image data recovery and pattern recognition under tensor framework, Pattern Recognition, vol. 122, pp. 108311, 2022.
Z. Chen, G. Zhou and Q. Zhao, Hierarchical factorization strategy for high-order tensor and application to data completion, IEEE Signal Processing Letters, vol. 28, pp. 1255-1259, 2021.
Y. Liu, Z. Long and C. Zhu, Image completion using low tensor tree rank and total variation minimization, IEEE Transactions on Multimedia, vol. 21, no. 2, pp. 338-350, 2019.
S. Du, B. Liu, G. Shan, Y. Shi, W. Wang, Enhanced tensor low-rank representation for clustering and denoising, Knowledge-Based Systems, vol. 243, pp. 108468, 2022.
M. E. Kilmer, C. D. Martin, Factorization strategies for third-order tensors, Linear Algebra and its Applications, vol. 435, no. 3, pp.641-658, 2011.
M. E. Kilmer, K. Braman, N. Hao, and R. C. Hoover, Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging, SIAM Journal on Matrix Analysis and Applications, vol. 34, no. 1, pp. 148-172, 2013.
C. Lu, J. Feng, Y. Chen, W. Liu, Z. Lin and S. Yan, Tensor robust principal component analysis with a new tensor nuclear norm, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 4, pp. 925-938, 2020.
H. Zhang, W. He, L. Zhang, H. Shen and Q. Yuan, Hyperspectral image restoration using low-rank matrix recovery, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 8, pp. 4729-4743, 2014.
B. Wen, S. Ravishankar and Y. Bresler, Video denoising by online 3D sparsifying transform learning, 2015 IEEE International Conference on Image Processing (ICIP), pp. 118-122, 2015.
Y.-R. Fan, T.-Z. Huang, Hyperspectral image restoration via superpixel segmentation of smooth band, Neurocomputing, 455, pp.340-352, 2021.
Q. Jiang, X.-L. Zhao, J. Lin, Y.-R. Fan, J. Peng, G.-C. Wu, Superpixel-based robust tensor low-rank approximation for multimedia data recover, Knowledge-Based Systems, 277 (110812), 2023.
E. J. Candes, X. Li, Y. Ma, and J. Wright, Robust principal component analysis?, Journal of the ACM (JACM), vol. 58, no. 3, pp. 1-37, 2011.
J. Liu, P. Musialski, P. Wonka, and J. Ye, Tensor completion for estimating missing values in visual data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 208-220, 2013.
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