Matlab implementations of the following paper:

@article{he2023weighted,<br>
  title={Weighted Order-p Tensor Nuclear Norm Minimization and Its Application to Hyperspectral Image Mixed Denoising},
  author={He, Chengxun and Cao, Qiujie and Xu, Yang and Sun, Le and Wu, Zebin and Wei, Zhihui},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2023},
  volume={20},
  publisher={IEEE},
  note = {Art no. 5510505}
}

I sincerely hope this trivial work is worth your precious time, if this code is helpful to you, please consider citing our work, thank you! Good luck with your research!
If there is anything we can help you with, please don't hesitate to contact us.

Since I accidentally forgot to save the random seeds used in the simulation, please manually download the data set in the paper to reproduce the results before running the code.
Link: https://drive.google.com/file/d/1hag_tBtpqsN-Fh9k716-OSF2gCpXeg80/view?usp=drive_link
or you can directly ask me on E-mail or WeChat.

Again, thank you for your valuable support, and I am very eager for your criticism and guidance.

Sincerely yours
Chengxun He
Nanjing University of Science and Technology

E-Mail: cx.he@njust.edu.cn (The previous address cxunhey@nuist.edu.cn is no longer valid since I have graduated from NUIST in 2022)
WeChat: cxunhey

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In this work, we introduce a weighted strategy into high-order tensor nuclear norm minimization and build an efficient subspace low-rank learning model to apply it to practical image restoration tasks.
The following excellent studies are the basis of our work, and we express our sincere gratitude to these outstanding researchers:

Qin W, Wang H, Zhang F, et al. Low-rank high-order tensor completion with applications in visual data[J]. IEEE Transactions on Image Processing, 2022, 31: 2433-2448.
Gu S, Xie Q, Meng D, et al. Weighted nuclear norm minimization and its applications to low level vision[J]. International journal of computer vision, 2017, 121: 183-208.
Zhuang L, Bioucas-Dias J M. Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(3): 730-742.
Bioucas-Dias J M, Nascimento J M P. Hyperspectral subspace identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8): 2435-2445.
Sun L, Jeon B. A novel subspace spatial-spectral low rank learning method for hyperspectral denoising[C]//2017 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2017: 1-4.