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Infrared Small Target Detection via tensor $L_{2,1}$ norm minimization and ASSTV regularization: A Novel Tensor Recovery Approach (2402.18003v1)

Published 28 Feb 2024 in math.NA and cs.NA

Abstract: In recent years, there has been a noteworthy focus on infrared small target detection, given its vital importance in processing signals from infrared remote sensing. The considerable computational cost incurred by prior methods, relying excessively on nuclear norm for noise separation, necessitates the exploration of efficient alternatives. The aim of this research is to identify a swift and resilient tensor recovery method for the efficient extraction of infrared small targets from image sequences. Theoretical validation indicates that smaller singular values predominantly contribute to constructing noise information. In the exclusion process, tensor QR decomposition is employed to reasonably reduce the size of the target tensor. Subsequently, we address a tensor $L_{2,1}$ Norm Minimization via T-QR (TLNMTQR) based method to effectively isolate the noise, markedly improving computational speed without compromising accuracy. Concurrently, by integrating the asymmetric spatial-temporal total variation regularization method (ASSTV), our objective is to augment the flexibility and efficacy of our algorithm in handling time series data. Ultimately, our method underwent rigorous testing with real-world data, affirmatively showcasing the superiority of our algorithm in terms of speed, precision, and robustness.

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References (32)
  1. R. R. Nadakuditi, “Optshrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage,” IEEE Transactions on Information Theory, vol. 60, no. 5, pp. 3002–3018, 2014.
  2. Z. Zhang, C. Ding, Z. Gao, and C. Xie, “Anlpt: Self-adaptive and non-local patch-tensor model for infrared small target detection,” Remote Sensing, vol. 15, no. 4, p. 1021, 2023.
  3. H. Yi, C. Yang, R. Qie, J. Liao, F. Wu, T. Pu, and Z. Peng, “Spatial-temporal tensor ring norm regularization for infrared small target detection,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023.
  4. L. Chuntong and W. Hao, “Research on infrared dim and small target detection algorithm based on low-rank tensor recovery,” Journal of Systems Engineering and Electronics, 2023.
  5. M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing mri,” IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 72–82, 2008.
  6. M. Rani, S. B. Dhok, and R. B. Deshmukh, “A systematic review of compressive sensing: Concepts, implementations and applications,” IEEE Access, vol. 6, pp. 4875–4894, 2018.
  7. V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, “Sparse and low-rank matrix decompositions,” IFAC Proceedings Volumes, vol. 42, no. 10, pp. 1493–1498, 2009, 15th IFAC Symposium on System Identification. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1474667016388632
  8. D. Bertsimas, R. Cory-Wright, and N. A. Johnson, “Sparse plus low rank matrix decomposition: A discrete optimization approach,” Journal of Machine Learning Research, vol. 24, no. 267, pp. 1–51, 2023.
  9. G. Daniel, G. Meirav, O. Noam, B.-K. Tamar, R. Dvir, O. Ricardo, and B.-E. Noam, “Fast and accurate t2 mapping using bloch simulations and low-rank plus sparse matrix decomposition,” Magnetic Resonance Imaging, vol. 98, pp. 66–75, 2023.
  10. Z. Lin, M. Chen, and Y. Ma, “The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices,” arXiv preprint arXiv:1009.5055, 2010.
  11. Y. Chen, Y. Guo, Y. Wang, D. Wang, C. Peng, and G. He, “Denoising of hyperspectral images using nonconvex low rank matrix approximation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 9, pp. 5366–5380, 2017.
  12. Y. Xie, Y. Qu, D. Tao, W. Wu, Q. Yuan, and W. Zhang, “Hyperspectral image restoration via iteratively regularized weighted schatten p𝑝pitalic_p-norm minimization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4642–4659, 2016.
  13. 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, 2013.
  14. J. Xue, Y. Zhao, S. Huang, W. Liao, J. C.-W. Chan, and S. G. Kong, “Multilayer sparsity-based tensor decomposition for low-rank tensor completion,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6916–6930, 2022.
  15. M. Wang, D. Hong, Z. Han, J. Li, J. Yao, L. Gao, B. Zhang, and J. Chanussot, “Tensor decompositions for hyperspectral data processing in remote sensing: A comprehensive review,” IEEE Geoscience and Remote Sensing Magazine, 2023.
  16. 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.
  17. 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.
  18. M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, “Total variation spatial regularization for sparse hyperspectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 11, pp. 4484–4502, 2012.
  19. V. Vishnevskiy, T. Gass, G. Szekely, C. Tanner, and O. Goksel, “Isotropic total variation regularization of displacements in parametric image registration,” IEEE Transactions on Medical Imaging, vol. 36, no. 2, pp. 385–395, 2017.
  20. W. He, H. Zhang, H. Shen, and L. Zhang, “Hyperspectral image denoising using local low-rank matrix recovery and global spatial–spectral total variation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 3, pp. 713–729, 2018.
  21. T. Liu, J. Yang, B. Li, C. Xiao, Y. Sun, Y. Wang, and W. An, “Nonconvex tensor low-rank approximation for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–18, 2022.
  22. S. Weiland and F. Van Belzen, “Singular value decompositions and low rank approximations of tensors,” IEEE transactions on signal processing, vol. 58, no. 3, pp. 1171–1182, 2009.
  23. Y. Zheng and A.-B. Xu, “Tensor completion via tensor qr decomposition and l2,1-norm minimization,” Signal Processing, vol. 189, p. 108240, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0165168421002772
  24. C. Gao, D. Meng, Y. Yang, Y. Wang, X. Zhou, and A. G. Hauptmann, “Infrared patch-image model for small target detection in a single image,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4996–5009, 2013.
  25. X. Wang, Z. Peng, D. Kong, P. Zhang, and Y. He, “Infrared dim target detection based on total variation regularization and principal component pursuit,” Image and Vision Computing, vol. 63, pp. 1–9, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0262885617300756
  26. L. Zhang and Z. Peng, “Infrared small target detection based on partial sum of the tensor nuclear norm,” Remote Sensing, vol. 11, no. 4, p. 382, 2019.
  27. Y. Sun, J. Yang, and W. An, “Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 3737–3752, 2020.
  28. X. Kong, C. Yang, S. Cao, C. Li, and Z. Peng, “Infrared small target detection via nonconvex tensor fibered rank approximation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–21, 2021.
  29. C.-I. Chang, “An effective evaluation tool for hyperspectral target detection: 3d receiver operating characteristic curve analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 6, pp. 5131–5153, 2021.
  30. S. Atapattu, C. Tellambura, and H. Jiang, “Analysis of area under the roc curve of energy detection,” IEEE Transactions on Wireless Communications, vol. 9, no. 3, pp. 1216–1225, 2010.
  31. S. Manti, M. K. Svendsen, N. R. Knøsgaard, P. M. Lyngby, and K. S. Thygesen, “Exploring and machine learning structural instabilities in 2d materials,” npj Computational Materials, vol. 9, no. 1, p. 33, 2023.
  32. B. Hui, Z. Song, H. Fan, P. Zhong, W. Hu, X. Zhang, J. Lin, H. Su, W. Jin, Y. Zhang, and Y. Bai, “A dataset for infrared image dim-small aircraft target detection and tracking under ground / air background,” Oct. 2019.

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