170
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Perceptually-motivated adversarial training for deep ensemble denoising of hyperspectral images

ORCID Icon &
Pages 767-777 | Received 02 Feb 2022, Accepted 08 May 2022, Published online: 31 May 2022

References

  • Aetesam, H., K. Poonam, and S. K. Maji. 2020. “Proximal Approach to Denoising Hyperspectral Images Under Mixed-Noise Model.” IET Image Processing 14 (14): 3366–3372. doi:https://doi.org/10.1049/iet-ipr.2019.1763.
  • Aetesam, H., S. Kumar Maji, and J. Boulanger. 2020. “A Two-Phase Splitting Approach for the Removal of Gaussian-Impulse Noise from Hyperspectral Images.” In International Conference on Computer Vision and Image Processing, 179–190. Springer.
  • Aetesam, H., S. Kumar Maji, and H. Yahia. 2021. “Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework.” IEEE Access 9: 169335–169347. doi:https://doi.org/10.1109/ACCESS.2021.3137656.
  • Arjovsky, M., S. Chintala, and L. Bottou. 2017. “Wasserstein Generative Adversarial Networks.” In International conference on machine learning. Proceedings of Machine Learning Research, Sydney, Australia, 70, 214–223.
  • Chang, Y., L. Yan, H. Fang, S. Zhong, and W. Liao. 2018. “HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 57 (2): 667–682. doi:https://doi.org/10.1109/TGRS.2018.2859203.
  • Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. “Generative Adversarial Nets.“ Advances in Neural Information Processing Systems, 27 (Curran Associates, Inc.). 2672–2680.
  • Gulrajani, I., F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville. 2017. “Improved Training of Wasserstein Gans.“ Advances in Neural Information Processing Systems, Long Beach, California, United States, 30, 5767–5777.
  • He, W., H. Zhang, L. Zhang, and H. Shen. 2015. “Total-Variation- Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration.” IEEE Transactions on Geoscience and Remote Sensing 54 (1): 178–188.
  • He, W., H. Zhang, H. Shen, and L. Zhang. 2018. “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 11 (3): 713–729. doi:https://doi.org/10.1109/JSTARS.2018.2800701.
  • Ioffe, S., and C. Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” In International conference on machine learning, Lille, France 32, 448–456. PMLR.
  • Ioffe, S. 2017. “Batch Renormalization: Towards Reducing Minibatch Dependence in Batch- Normalized Models.“ Advances in Neural Information Processing Systems Long Beach, California, United States, 30, 1945–1953.
  • Kumar, S., H. Aetesam, A. Saha, and S. Kumar Maji. 2021. “Attention- Based Deep Autoencoder for Hyperspectral Image Denoising.” In IAPR International Conference on Computer Vision & Image Processing CVIP. 6, Singapore: Springer, IIT Ropar, India.
  • Ledig, C., L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, et al. 2017. “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, United States: IEEE, 4681–4690.
  • Lin, B., X. Tao, and J. Lu. 2019. “Hyperspectral Image Denoising via Matrix Factorization and Deep Prior Regularization.” IEEE Transactions on Image Processing 29: 565–578. doi:https://doi.org/10.1109/TIP.2019.2928627.
  • Maffei, A., J. M. Haut, M. Eugenia Paoletti, J. Plaza, L. Bruzzone, and A. Plaza. 2019. “A Single Model CNN for Hyperspectral Image Denoising.” IEEE Transactions on Geoscience and Remote Sensing 58 (4): 2516–2529. doi:https://doi.org/10.1109/TGRS.2019.2952062.
  • Othman, H., and S.-E. Qian. 2006. “Noise Reduction of Hyperspectral Imagery Using Hybrid Spatial-Spectral Derivative-Domain Wavelet Shrinkage.” IEEE Transactions on Geoscience and Remote Sensing 44 (2): 397–408. doi:https://doi.org/10.1109/TGRS.2005.860982.
  • Shi, Q., X. Tang, T. Yang, R. Liu, and L. Zhang. 2021. “Hyperspectral Image Denoising Using a 3-D Attention Denoising Network.“ IEEE Transactions on Geoscience and Remote Sensing, 59 (12): 10348–10363. doi:https://doi.org/10.1109/TGRS.2020.3045273
  • Sidorov, O., and J. Yngve Hardeberg. 2019. “Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution.” In Proceedings of the IEEE International Conference on Computer Vision Workshops Seoul, Korea.
  • Sun, L., G. Zhao, Y. Zheng, and Z. Wu. 2022. “Spectral-Spatial Feature Tokenization Transformer for Hyperspectral Image Classification.“ IEEE Transactions on Geoscience and Remote Sensing 60: 5522214. doi:https://doi.org/10.1109/TGRS.2022.3144158
  • Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. “Image Quality Assessment: From Error Visibility to Structural Similarity.” IEEE Transactions on Image Processing 13 (4): 600–612. doi:https://doi.org/10.1109/TIP.2003.819861.
  • Wang, M., Q. Wang, and J. Chanussot. 2021. “Tensor Low-Rank Constraint and L 0 Total Variation for Hyperspectral Image Mixed Noise Removal.” IEEE Journal of Selected Topics in Signal Processing 15 (3): 718–733. doi:https://doi.org/10.1109/JSTSP.2021.3058503.
  • Xiong, F., J. Zhou, Q. Zhao, J. Lu, and Y. Qian. 2021. “MAC-Net: Model Aided Nonlocal Neural Network for Hyperspectral Image Denoising IEEE Transactions on Geoscience and Remote Sensing 60: 5519414. doi:https://doi.org/10.1109/TGRS.2021.3131878
  • Xu, F., Y. Chen, C. Peng, Y. Wang, X. Liu, and G. He. 2017. “Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization.” IEEE Transactions on Geoscience and Remote Sensing 14 (7): 1141–1145. doi:https://doi.org/10.1109/LGRS.2017.2700406.
  • Yang, F., X. Chen, and L. Chai. 2021. “Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation.” Remote Sensing 13 (4): 827. doi:https://doi.org/10.3390/rs13040827.
  • Ye, R., F. Liu, and L. Zhang. 2019. “3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks.” In Canadian Conference on Artificial Intelligence Kingston, Ontario, Canada, 186–199. Switzerland: Springer Nature.
  • Yuan, Q., Q. Zhang, J. Li, H. Shen, and L. Zhang. 2018. “Hyperspectral Image Denoising Employing a Spatial–spectral Deep Residual Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 57 (2): 1205–1218. doi:https://doi.org/10.1109/TGRS.2018.2865197.
  • Zhao, H., O. Gallo, I. Frosio, and J. Kautz. 2016. “Loss Functions for Image Restoration with Neural Networks.” IEEE Transactions on Computational Imaging 3 (1): 47–57. doi:https://doi.org/10.1109/TCI.2016.2644865.
  • Zheng, Y.-B., T.-Z. Huang, X.-L. Zhao, Y. Chen, and W. He. 2020. “Double-Factor-Regularized Low-Rank Tensor Factorization for Mixed Noise Removal in Hyperspectral Image IEEE Transactions on Geoscience and Remote Sensing 58 (12): 8450- 8464. doi:https://doi.org/10.1109/TGRS.2020.2987954

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.