基于重叠组稀疏超拉普拉斯正则化的高光谱图像恢复
Hyperspectral Image Restoration Based on Overlapping Group Sparse Hyper Laplacian Regularization
摘要: 高光谱图像混合噪声去除是遥感领域的一个基本问题，也是一个重要的预处理步骤。本研究针对高光谱图像去噪问题，为有效地对高光谱图像进行恢复，提出了一种基于重叠组稀疏性超拉普拉斯正则化(OGS-HL)的新型去噪方法。该方法可以有效捕捉图像的局部相关性和方向性结构，同时减少传统全变分正则化中的阶梯伪影。通过乘子交替方向法求解非凸优化问题，显著提高了去噪效率。在多个遥感图像数据集上的仿真实验表明，所提方法在峰值信噪比(PSNR)和结构相似度(SSIM)等评价指标上优于现有技术，展现了在复杂噪声环境下的优越去噪性能和广泛的应用潜力。
Abstract: The removal of mixed noise from hyperspectral images is a fundamental issue in the field of remote sensing and an important preprocessing step. This study focuses on the denoising problem of hyperspectral images. To effectively restore hyperspectral images, a new denoising method based on Overlap Group Sparse Hyper Laplacian Regularization (OGS-HL) is proposed. This method can effectively capture the local correlation and directional structure of images, while reducing the step artifacts in traditional total variation regularization. By using the alternating direction method of multipliers to solve non-convex optimization problems, the denoising efficiency has been significantly improved. Simulation experiments on multiple remote sensing image datasets have shown that the proposed method outperforms existing technologies in evaluation metrics such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), demonstrating superior denoising performance and broad application potential in complex noisy environments.
文章引用：冉启刚. 基于重叠组稀疏超拉普拉斯正则化的高光谱图像恢复[J]. 应用数学进展, 2024, 13(9): 4307-4321. https://doi.org/10.12677/aam.2024.139411

参考文献

[1] Chang, C.-I. (1999) Spectral Information Divergence for Hyperspectral Image Analysis. IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg, 28 June-2 July 1999, 509-511.
[2] Manolakis, D., Marden, D. and Shaw, G.A. (2003) Hyperspectral Image Processing for Automatic Target Detection Applications. Lincoln Lab, 14, 79-116.
[3] Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., et al. (2017) Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art. IEEE Geoscience and Remote Sensing Magazine, 5, 37-78. [Google Scholar] [CrossRef] 
[4] Crouse, M.S., Nowak, R.D. and Baraniuk, R.G. (1998) Wavelet-Based Statistical Signal Processing Using Hidden Markov Models. IEEE Transactions on Signal Processing, 46, 886-902. [Google Scholar] [CrossRef] 
[5] Li, Y., Wu, Z., Wei, J., Plaza, A., Li, J. and Wei, Z. (2015) Fast Principal Component Analysis for Hyperspectral Imaging Based on Cloud Computing. 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, 26-31 July 2015, 513-516. [Google Scholar] [CrossRef] 
[6] Dev Vishnu, S., Rajan, S., Sowmya, V. and Soman, K.P. (2017) Hyperspectral Image Denoising: A Least Square Approach Using Wavelet Filters. 2017 International Conference on Advances in Computing, Communications and Informatics, Udupi, 13-16 September 2017, 805-811. [Google Scholar] [CrossRef] 
[7] Letexier, D. and Bourennane, S. (2008) Multidimensional Wiener Filtering Using Fourth Order Statistics of Hyperspectral Images. 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, 31 March-4 April 2008, 917-920. [Google Scholar] [CrossRef] 
[8] Licciardi, G.A. and Chanussot, J. (2015) Nonlinear PCA for Visible and Thermal Hyperspectral Images Quality Enhancement. IEEE Geoscience and Remote Sensing Letters, 12, 1228-1231. [Google Scholar] [CrossRef] 
[9] Meng, S., Huang, L. and Wang, W. (2016) Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising. IEEE Geoscience and Remote Sensing Letters, 13, 897-901. [Google Scholar] [CrossRef] 
[10] Alexey, P. and Olga, P. (2024) Real Time Method and Algorithms for Fast Discrete Fourier Transform of Discrete Finite Signals. 2024 26th International Conference on Digital Signal Processing and its Applications, Moscow, 27-29 March 2024, 1-6. [Google Scholar] [CrossRef] 
[11] Metzler, C.A., Maleki, A. and Baraniuk, R.G. (2016) BM3D-PRGAMP: Compressive Phase Retrieval Based on BM3D Denoising. 2016 IEEE International Conference on Image Processing, Phoenix, 25-28 September 2016, 2504-2508. [Google Scholar] [CrossRef] 
[12] Rudin, L.I., Osher, S. and Fatemi, E. (1992) Nonlinear Total Variation Based Noise Removal Algorithms. Physica D: Nonlinear Phenomena, 60, 259-268. [Google Scholar] [CrossRef] 
[13] Wen, Y., Ng, M.K. and Huang, Y. (2008) Efficient Total Variation Minimization Methods for Color Image Restoration. IEEE Transactions on Image Processing, 17, 2081-2088. [Google Scholar] [CrossRef] [PubMed]
[14] Ye, M., Qian, Y. and Zhou, J. (2015) Multitask Sparse Nonnegative Matrix Factorization for Joint Spectral-Spatial Hyperspectral Imagery Denoising. IEEE Transactions on Geoscience and Remote Sensing, 53, 2621-2639. [Google Scholar] [CrossRef] 
[15] Liang, H. (2015) Research of Liquid CT Image De-Noising Based on Improved NL-Means Algorithm. 2015 International Carnahan Conference on Security Technology, Taipei, 21-24 September 2015, 359-362. [Google Scholar] [CrossRef] 
[16] Wang, Y., Lin, L., Zhao, Q., Yue, T., Meng, D. and Leung, Y. (2017) Compressive Sensing of Hyperspectral Images via Joint Tensor Tucker Decomposition and Weighted Total Variation Regularization. IEEE Geoscience and Remote Sensing Letters, 14, 2457-2461. [Google Scholar] [CrossRef] 
[17] Tian, X., Xie, K. and Zhang, H. (2024) Hyperspectral Image Denoising via L0 Regularized Low-Rank Tucker Decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 3297-3313. [Google Scholar] [CrossRef] 
[18] Lu, X., Wang, Y. and Yuan, Y. (2013) Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images. IEEE Transactions on Geoscience and Remote Sensing, 51, 4009-4018. [Google Scholar] [CrossRef] 
[19] Xu, Y., Song, X., Dong, F. and Wang, H. (2013) An Adaptive Total Variation Regularization Method for Electrical Resistance Tomography. 2013 IEEE International Conference on Imaging Systems and Techniques, Beijing, 22-23 October 2013, 127-131. [Google Scholar] [CrossRef] 
[20] Liu, J., Huang, T., Selesnick, I.W., Lv, X. and Chen, P. (2015) Image Restoration Using Total Variation with Overlapping Group Sparsity. Information Sciences, 295, 232-246. [Google Scholar] [CrossRef] 
[21] Jon, K., Sun, Y., Li, Q., Liu, J., Wang, X. and Zhu, W. (2021) Image Restoration Using Overlapping Group Sparsity on Hyper-Laplacian Prior of Image Gradient. Neurocomputing, 420, 57-69. [Google Scholar] [CrossRef] 
[22] Chen, Y., Huang, T. and Zhao, X. (2018) Destriping of Multispectral Remote Sensing Image Using Low-Rank Tensor Decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 4950-4967. [Google Scholar] [CrossRef] 
[23] Su, X., Zhang, Z. and Yang, F. (2023) Fast Hyperspectral Image Denoising and Destriping Method Based on Graph Laplacian Regularization. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-14. [Google Scholar] [CrossRef] 
[24] Chen, Y., Cao, W., Pang, L., Peng, J. and Cao, X. (2023) Hyperspectral Image Denoising via Texture-Preserved Total Variation Regularizer. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-14. [Google Scholar] [CrossRef] 
[25] Zheng, Y., Huang, T., Zhao, X., Chen, Y. and He, W. (2020) Double-Factor-Regularized Low-Rank Tensor Factorization for Mixed Noise Removal in Hyperspectral Image. IEEE Transactions on Geoscience and Remote Sensing, 58, 8450-8464. [Google Scholar] [CrossRef] 
[26] Peng, J., Wang, H., Cao, X., Liu, X., Rui, X. and Meng, D. (2022) Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17. [Google Scholar] [CrossRef] 
[27] Chang, Y., Yan, L.X., Fang, H.Z. and Luo, C.N. (2015) Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping. IEEE Transactions on Image Processing, 24, 1852-1866. [Google Scholar] [CrossRef] [PubMed]

为你推荐