A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 3
IEEE/CAA Journal of Automatica Sinica
| Citation: | E. T. Pan, Y. Ma, X. G. Mei, J. Huang, F. Fan, and J. Y. Ma, “D2Net: Deep denoising network in frequency domain for hyperspectral image,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 813–815, Mar. 2023. doi: 10.1109/JAS.2022.106019
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| [1] |
W. Cho, J. Jang, A. Koschan, M. A. Abidi, and J. Paik, “Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models,” Optics Express, vol. 24, no. 24, pp. 27637–27662, 2016.
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| [2] |
C. Wu, B. Du, and L. Zhang, “Hyperspectral anomalous change detection based on joint sparse representation,” ISPRS J. Photo- grammetry and Remote Sensing, vol. 146, pp. 137–150, 2018. doi: 10.1016/j.isprsjprs.2018.09.005
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| [3] |
L. Zhuang and J. M. Bioucas-Dias, “Hyperspectral image denoising based on global and non-local low-rank factorizations,” in Proc. IEEE Int. Conf. Image Processing, 2017, pp. 1900–1904.
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| [4] |
M. Maggioni, V. Katkovnik, K. Egiazarian, and A. Foi, “Nonlocal transform-domain filter for volumetric data denoising and reconstruction,” IEEE Trans. Image Processing, vol. 22, no. 1, pp. 119–133, 2013.
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| [5] |
L. Geng, Z. Ji, Y. Yuan, and Y. Yin, “Fractional-order sparse representation for image denoising,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 555–563, 2018. doi: 10.1109/JAS.2017.7510412
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| [6] |
Q. Xie, Q. Zhao, D. Meng, Z. Xu, S. Gu, W. Zuo, and L. Zhang, “Multispectral images denoising by intrinsic tensor sparsity regularization,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, 2016, pp. 1692–1700.
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| [7] |
Y. Peng, D. Meng, Z. Xu, C. Gao, Y. Yang, and B. Zhang, “Decomposable nonlocal tensor dictionary learning for multispectral image denoising,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2014, pp. 2949–2956.
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| [8] |
Q. Lian, W. Yan, X. Zhang, and S. Chen, “Single image rain removal using image decomposition and a dense network,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1428–1437, 2019.
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| [9] |
K. Wei, Y. Fu, and H. Huang, “3-D quasi-recurrent neural network for hyperspectral image denoising,” IEEE Trans. Neural Networks and Learning Systems, vol. 32, no. 1, pp. 363–375, 2021.
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| [10] |
Q. Yuan, Q. Zhang, J. Li, H. Shen, and L. Zhang, “Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network,” IEEE Trans. Geoscience and Remote Sensing, vol. 57, no. 2, pp. 1205–1218, 2019.
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| [11] |
Z. Hui, X. Gao, Y. Yang, and X. Wang, “Lightweight image super-resolution with information multi-distillation network,” in Proc. 27th ACM Int. Conf. Multimedia, 2019, pp. 2024–2032.
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| [12] |
W. Liu, Q. Yan, and Y. Zhao, “Densely self-guided wavelet network for image denoising,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition Workshops, 2020, pp. 1742–1750.
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