Open Access
E3S Web Conf.
Volume 275, 2021
2021 International Conference on Economic Innovation and Low-carbon Development (EILCD 2021)
Article Number 03018
Number of page(s) 6
Section Environmental Protection and Governance Innovation Technology Research
Published online 21 June 2021
  1. Xie T, Li S, Sun B. Hyperspectral images denoising via nonconvex regularized low-rank and sparse matrix decomposition[J]. IEEE Transactions on Image Processing, 2019, 29(1): 44-56. [CrossRef] [Google Scholar]
  2. Lee S, Negishi M, Urakubo H, et al. Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration[J]. Neural Networks, 2020, 125(5): 92-103. [CrossRef] [Google Scholar]
  3. Zhang L, Wang J, An Z. Classification method of CO2 hyperspectral remote sensing data based on neural network[J]. Computer Communications, 2020, 156(5): 124-130. [CrossRef] [Google Scholar]
  4. Li Y, Xu J, Xia R, et al. A two-stage framework of target detection in high-resolution hyperspectral images[J]. Signal, Image and Video Processing, 2019, 13(7): 1339-1346. [CrossRef] [Google Scholar]
  5. Fan H, Li J, Yuan Q, et al. Hyperspectral image denoising with bilinear low rank matrix factorization[J]. Signal Processing, 2019, 163: 132-152. [CrossRef] [Google Scholar]
  6. Xing L, Chang Q, Qiao T. The algorithms about fast non-localmeans based image denoising[J]. Acta Mathematicae Applicatae Sinica, English Series, 2019, 28(2): 247-254. [CrossRef] [Google Scholar]
  7. Baloch G, Ozkaramanli H. Image denoising via correlation-based sparse representation[J]. Signal, Image and Video Processing, 2017, 11(8): 1501-1508. [CrossRef] [Google Scholar]
  8. Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on image processing, 2017, 16(8): 2080-2095. [NASA ADS] [CrossRef] [Google Scholar]
  9. Othman H, Qian S E. Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 44(2): 397-408. [CrossRef] [Google Scholar]
  10. Lu C, Tang J, Yan S, et al. Nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm[J]. IEEE Transactions on Image Processing, 2019, 25(2): 829-839. [CrossRef] [Google Scholar]
  11. Li C, Ma Y, Huang J, et al. Hyperspectral image denoising using the robust low-rank tensor recovery[J]. Journal of the Optical Society of America A, 2019, 32(9): 1604-1612. [CrossRef] [Google Scholar]
  12. Renard N, Bourennane S, Blanc-TALON J. Denoising and dimensionality reduction using multilinear tools for hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 5(2): 138-142. [CrossRef] [Google Scholar]
  13. Kong X, Zhao Y, Xue J, et al. Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation[J]. Remote Sensing, 2019, 11(19): 2281-2303. [CrossRef] [Google Scholar]
  14. Zhang H, He W, Zhang L, et al. Hyperspectral image restoration using low-rank matrix recovery[J]. IEEE transactions on geoscience and remote sensing, 2019, 52(8): 4729-4743. [CrossRef] [Google Scholar]
  15. CandÈS E J, Li X, Ma Y, et al. Robust principal component analysis?[J]. Journal of the ACM, 2019, 58(3): 31-37. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.