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Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise arising from intra-imaging mechanisms and environmental factors.
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W. Dong, H. Wang, F. Wu, G. Shi, and X. Li, “Deep spatial–spectral representation learning for hyperspectral image denoising,” IEEE Trans. Comput. Imag. , vol. 5, no. 4, pp. 635–648, 2019
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F. Wang, J. Li, Q. Yuan, and L. Zhang, “Local-global feature-aware transformer based residual network for hyperspectral image denoising,”
2022
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2022
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C. Wang, M. Xu, Y. Jiang, G. Zhang, H. Cui, L. Li, and D. Li, “Translution-SNet: A semisupervised hyperspectral image stripe noise removal based on transformer and CNN,”
2022
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Y. Cai, J. Lin, X. Hu, H. Wang, X. Yuan, Y. Zhang, R. Timofte, and L. Van Gool, “Mask-Guided spectral-wise transformer for efficient hyperspectral image reconstruction,” in
2022
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F. Xiong, J. Zhou, Q. Zhao, J. Lu, and Y. Qian, “MAC-Net: Model-aided nonlocal neural network for hyperspectral image denoising,”
2022
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X. Yang, B. Tu, Q. Li, J. Li, and A. Plaza, “Graph evolution-based vertex extraction for hyperspectral anomaly detection,” IEEE Trans. Neural Netw. Learn. Syst. , pp. 1–15, 2023
2023
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L. Gao, X. Sun, X. Sun, L. Zhuang, Q. Du, and B. Zhang, “Hyperspectral anomaly detection based on chessboard topology,” IEEE Trans. Geosci. Remote Sens. , vol. 61, pp. 1–16, 2023
2023
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J. Wang, K. C. Chan, and C. C. Loy, “Exploring CLIP for assessing the look and feel of images,” in
2023
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Z. Li, F. Xiong, J. Zhou, J. Lu, Z. Zhao, and Y. Qian, “Material-guided multiview fusion network for hyperspectral object tracking,” IEEE Trans. Geosci. Remote Sens. , vol. 62, pp. 1–15, 2024
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