Joint weighted nuclear norm and total variation regularization for hyperspectral image denoising

References

• Aggarwal, H. K., and A. Majumdar. 2016. “Hyperspectral Image Denoising Using Spatio-Spectral Total Variation.” IEEE Geoscience and Remote Sensing Letters 13 (3): 442–446. doi:10.1109/LGRS.2016.2518218.
   Web of Science ®Google Scholar
• Beck, A., and M. Teboulle. 2009. “Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems.” IEEE Transactions on Image Processing 18 (11): 2419–2434. doi:10.1109/TIP.2009.2028250.
   PubMed Web of Science ®Google Scholar
• Bioucasdias, J. M., and J. M. Nascimento. 2008. “Hyperspectral Subspace Identification.” IEEE Transactions on Geoscience and Remote Sensing 46 (8): 2435–2445. doi:10.1109/TGRS.2008.918089.
   Web of Science ®Google Scholar
• Bioucasdias, J. M., A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. D. Gader, and J. Chanussot. 2012. “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (2): 354–379. doi:10.1109/JSTARS.2012.2194696.
   Web of Science ®Google Scholar
• Boyd, S., N. Parikh, E. Chu, B. Peleato, and J. Eckstein. 2011. “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers.” Foundations and Trends® in Machine Learning 3 (1): 1–122. doi:10.1561/2200000016.
   Google Scholar
• Buades, A., B. Coll, and J. M. Morel. 2005. “A Non-Local Algorithm for Image Denoising.” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2 (7):60–65. doi: 10.1109/CVPR.2005.38
   Google Scholar
• Cai, J., E. J. Candès, and Z. Shen. 2010. “A Singular Value Thresholding Algorithm for Matrix Completion.” SIAM Journal on Optimization 20 (4): 1956–1982. doi:10.1137/080738970.
   Web of Science ®Google Scholar
• Candès, E. J., X. Li, Y. Ma, and J. Wright. 2011. “Robust Principal Component Analysis.” Journal of the ACM 58 (3): 11. doi:10.1007/978-0-387-87811-9_3.
   Web of Science ®Google Scholar
• Candès, E. J., M. B. Wakin, and S. P. Boyd. 2008. “Enhancing Sparsity by Reweighted ℓ1 Minimization.” Journal of Fourier Analysis and Applications 14 (5–6): 877–905. doi:10.1007/s00041-008-9045-x.
   Web of Science ®Google Scholar
• Chen, G. Y., and S. E. Qian. 2009. “Denoising and Dimensionality Reduction of Hyperspectral Imagery Using Wavelet Packets, Neighbour Shrinking and Principal Component Analysis.” International Journal of Remote Sensing 30 (18): 4889–4895. doi:10.1080/01431160802653724.
   Web of Science ®Google Scholar
• Cheng, G., P. Zhou, and J. Han. 2016. “Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 54 (12): 7405–7415. doi:10.1109/TGRS.2016.2601622.
   Web of Science ®Google Scholar
• Dabov, K., A. Foi, V. Katkovnik, and K. Egiazarian. 2007. “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering.” IEEE Transactions on Image Processing 16 (8): 2080–2095. doi:10.1109/TIP.2007.901238.
   PubMed Web of Science ®Google Scholar
• Dong, W., L. Zhang, G. Shi, and X. Li. 2013. “Nonlocally Centralized Sparse Representation for Image Restoration.” IEEE Transactions on Image Processing 22 (4): 1620–1630. doi:10.1109/TIP.2012.2235847.
   PubMed Web of Science ®Google Scholar
• Golbabaee, M., and P. Vandergheynst. 2012. “Hyperspectral Image Compressed Sensing via Low-Rank and Joint-Sparse Matrix Recovery.” IEEE International Conference on Acoustics, Speech and Signal Processing. 2741–2744. doi: 10.1109/ICASSP.2012.6288484
   Google Scholar
• Gu, S., L. Zhang, W. Zuo, and X. Feng, 2014. “Weighted Nuclear Norm Minimization with Application to Image Denoising.” IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2862–2869. doi: 10.1109/CVPR.2014.366
   Google Scholar
• He, W., H. Zhang, L. Zhang, and H. Shen. 2015. “Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (6): 3050–3061. doi:10.1109/JSTARS.2015.2398433.
   Web of Science ®Google Scholar
• He, W., H. Zhang, L. Zhang, and H. Shen. 2016. “Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration.” IEEE Transactions on Geoscience and Remote Sensing 54 (1): 178–188. doi:10.1109/TGRS.2015.2452812.
   Web of Science ®Google Scholar
• Hu, F., G. Xia, Z. Wang, X. Huang, L. Zhang, and H. Sun. 2017. “Unsupervised Feature Learning via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 8 (5): 2015–2030. doi:10.1109/JSTARS.2015.2444405.
   Web of Science ®Google Scholar
• Li, J., H. Zhang, L. Zhang, X. Huang, and L. Zhang. 2014. “Joint Collaborative Representation with Multitask Learning for Hyperspectral Image Classification.” IEEE Transactions on Geoscience & Remote Sensing 52 (9): 5923–5936. doi:10.1109/TGRS.2013.2274875.
   Web of Science ®Google Scholar
• Lin, Z., M. Chen, and Y. Ma. 2010. “The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices.” Eprint Arxiv 9: 1–23. doi:10.1016/j.jsb.2012.10.010.
   Web of Science ®Google Scholar
• Lu, C., J. Tang, S. Yan, and Z. Lin. 2016. “Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm.” IEEE Transactions on Image Processing 25 (2): 829–839. doi:10.1109/TIP.2015.2511584.
   PubMed Web of Science ®Google Scholar
• Lu, X., Y. Yuan, and P. Yan. 2014. “Alternatively Constrained Dictionary Learning for Image Super-Resolution.” IEEE Transactions on Cybernetics 44 (3): 366–377. doi:10.1109/TCYB.2013.2256347.
   PubMed Web of Science ®Google Scholar
• Lu, X., Y. Yuan, and X. Zheng. 2017. “Remote Sensing Scene Classification by Unsupervised Representation Learning.” IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2017.2702596.
   Web of Science ®Google Scholar
• Mairal, J., F. Bach, J. Ponce, G. Sapiro, and A. Zisserman 2009. “Non-Local Sparse Models for Image Restoration.” IEEE International Conference on Computer Vision 30:2272–2279. doi: 10.1109/ICCV.2009.5459452
   Google Scholar
• Melgani, F., and L. Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (8): 1778–1790. doi:10.1109/TGRS.2004.831865.
   Web of Science ®Google Scholar
• Nasrabadi, N. M. 2014. “Hyperspectral Target Detection: An Overview of Current and Future Challenges.” IEEE Signal Processing Magazine 31 (1): 34–44. doi:10.1109/MSP.2013.2278992.
   Web of Science ®Google Scholar
• Parekh, A., and I. Selesnick. 2015. “Enhanced Low-Rank Matrix Approximation.” IEEE Signal Processing Letters 23 (4): 493–497. doi:10.1109/LSP.2016.2535227.
   Web of Science ®Google Scholar
• Peng, Y., J. Suo, Q. Dai, and W. Xu. 2014. “Reweighted Low-Rank Matrix Recovery and Its Application in Image Restoration.” IEEE Transactions on Systems, Man, and Cybernetics 44 (12): 2418–2430. doi:10.1109/TCYB.2014.2307854.
   Web of Science ®Google Scholar
• Rudin, L. I., S. Osher, and E. Fatemi. 1992. “Nonlinear Total Variation Based Noise Removal Algorithms.” Physica D Nonlinear Phenomena 60 (1–4): 259–268. doi:10.1016/0167-2789(92)90242-F.
   Web of Science ®Google Scholar
• Shabalin, A. A., and A. B. Nobel. 2010. “Reconstruction of a Low-Rank Matrix in the Presence of Gaussian Noise.” Journal of Multivariate Analysis 118 (5): 67–76. doi:10.1016/j.jmva.2013.03.005.
   Web of Science ®Google Scholar
• Slavkovikj, V., S. Verstockt, W. De Neve, S. V. Hoecke, and R. V. De Walle. 2016. “Unsupervised Spectral Sub-Feature Learning for Hyperspectral Image Classification.” International Journal of Remote Sensing 37 (2): 309–326. doi:10.1080/01431161.2015.1125554.
   Web of Science ®Google Scholar
• Wang, N., B. Du, and L. Zhang. 2013. “An Endmember Dissimilarity Constrained Non-Negative Matrix Factorization Method for Hyperspectral Unmixing.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 6 (2): 554–569. doi:10.1109/JSTARS.2013.2242255.
   Web of Science ®Google Scholar
• Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13 (4): 600–612. doi:10.1109/TIP.2003.819861.
   PubMed Web of Science ®Google Scholar
• Wu, Z., Q. Wang, Z. Wu, and Y. Shen. 2016. “Total Variation-Regularized Weighted Nuclear Norm Minimization for Hyperspectral Image Mixed Denoising.” Journal of Electronic Imaging 25 (1): 13037–13037. doi:10.1117/1.JEI.25.1.013037.
   Web of Science ®Google Scholar
• Xu X, Zhong Y, Zhang L, and H. Zhang. 2013. “Sub-Pixel Mapping Based on a MAP Model with Multiple Shifted Hyperspectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (2): 580–593. doi:10.1109/JSTARS.2012.2227246.
   Web of Science ®Google Scholar
• Yuan, Q., L. Zhang, and H. Shen. 2012. “Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model.” IEEE Transactions on Geoscience and Remote Sensing 50 (10): 3660–3677. doi:10.1109/TGRS.2012.2185054.
   Web of Science ®Google Scholar
• Yuan, Q., L. Zhang, and H. Shen. 2014. “Hyperspectral Image Denoising with a Spatial–Spectral View Fusion Strategy.” IEEE Transactions on Geoscience and Remote Sensing 52 (5): 2314–2325. doi:10.1109/TGRS.2013.2259245.
   Web of Science ®Google Scholar
• Zhang, H., W. He, L. Zhang, H. Shen, and Q. Yuan. 2014. “Hyperspectral Image Restoration Using Low-Rank Matrix Recovery.” IEEE Transactions on Geoscience and Remote Sensing 52 (8): 4729–4743. doi:10.1109/TGRS.2013.2284280.
   Web of Science ®Google Scholar
• Zhao, Y., L. Zhang, P. Li, and B. Huang. 2007. “Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features.” IEEE Transactions on Geoscience & Remote Sensing 45 (5): 1458–1468. doi:10.1109/TGRS.2007.892602.
   Web of Science ®Google Scholar
• Zhong, Y., L. Zhang, J. Gong, and P. Li. 2007. “A Supervised Artificial Immune Classifier for Remote-Sensing Imagery.” IEEE Transactions on Geoscience and Remote Sensing 45 (12): 3957–3966. doi:10.1109/TGRS.2007.907739.
   Web of Science ®Google Scholar
• Zhou, T., and D. Tao, 2011. “GoDec: Randomized Lowrank and Sparse Matrix Decomposition in Noisy Case.” International Conference on Machine Learning, Bellevue, Washington, USA, June 28 – July 02. 33–40.
   Google Scholar