ARCHIVES

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Magazine, vol. 1, no. 2, pp. 6–36, Jun. 2013.
2. P. Ghamisi et al., “Advances in hyperspectral image and signal processing: A comprehensive overview,” IEEE Geoscience and Remote
Sensing Magazine, vol. 5, no. 4, pp. 37–78, Dec. 2017.
3. L. Loncan et al., “Hyperspectral pansharpening: A review,” IEEE Geoscience and Remote Sensing Magazine, vol. 3, no. 3, pp. 27–46, Sep.
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Remote Sens., vol. 55, no. 5, pp. 2675–2689, May 2017.
5. S. Mei, J. Ji, Y. Geng, Z. Zhang, X. Li, and Q. Du, “Spatial–spectral fusion based on deep learning for hyperspectral image super-resolution,”
IEEE Trans. Geosci. Remote Sens., vol. 55, no. 12, pp. 6583–6595, Dec. 2017.
6. Q. Xie, Q. Zhao, D. Meng, and Z. Xu, “Hyperspectral image super-resolution using deep convolutional neural network,” Neurocomputing,
vol. 335, pp. 121–132, Mar. 2019.
7. Y. Yuan, J. Lin, and Q. Wang, “Hyperspectral image super-resolution: An overview and survey,” IEEE Signal Processing Magazine, vol. 37,
no. 6, pp. 95–107, Nov. 2020.
8. D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proc. IEEE CVPR, 2018, pp. 9446–9454.
9. O. Sidorov and J. Y. Hardeberg, “Deep hyperspectral prior: Single-image denoising, inpainting, super-resolution,” in Proc. ICCVW, 2019,
pp. 384–391.
10. Y. Zhang, L. Zhang, and J. Yang, “Unsupervised hyperspectral image super-resolution via deep image prior,” Remote Sensing, vol. 12, no.
7, p. 1197, Apr. 2020.
11. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. MICCAI, 2015, pp.
234–241.
12. D. Liu, J. Wang, Q. Shan, D. Smyl, J. Deng, and J. Du, “DeepEIT: Deep Image Prior Enabled Electrical Impedance Tomography,” IEEE
Trans. Pattern Anal. Mach. Intell., vol. 45, no. 8, pp. 9627–9638, 2023.
13. C. K. Reddy, A. Daduvy, R. M. Mohana, B. Assiri, M. Shuaib, S. Alam, and M. A. Sheneamer, “Enhancing Precision Agriculture and Land
Cover Classification: A Self-Attention 3D Convolutional Neural Network Approach for Hyperspectral Image Analysis,” IEEE Access, vol.
12, pp. 125592–125608, 2024.
14. J. Xu, S. You, Y. Guo, and Y. Fan, “Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Denoising,”IEEE Access, vol. 11, pp. 67912–67921, 2023.
15. H. R. Iglesias-Goldaracena, I. Ramı́rez, and E. Schiavi, “RD-DIP: Rician denoising deep image prior,” 2025.
16. J. Park, S. Cook, D. Lee, J. Choi, S. Yoo, S. Bae, H.-J. Im, D. Lee, and H. Choi, “Generation of super-resolution images from barcode-based
spatial transcriptomics by deep image prior,” 2025.
17. A. Naseer, N. Al Mudawi, M. Abdelhaq, M. Alonazi, A. Alazeb, A. Algarni, and A. Jalal, “CNN-Based Object Detection via Segmentation
Capabilities in Outdoor Natural Scenes,” 2024.
18. H. Yoo, P. M. Hong, T. Kim, J. W. Yoon, and Y. K. Lee, “Defending Against Adversarial Fingerprint Attacks Based on Deep Image Prior,”
2023.
19. J. He, Q. Yuan, J. Li, Y. Xiao, and L. Zhang, “A self-supervised remote sensing image fusion framework with dual-stage self-learning and
spectral super-resolution injection,” ISPRS J. Photogramm. Remote Sens., vol. 204, pp. 131–144, Oct. 2023.
20. G. Vivone, “Multispectral and hyperspectral image fusion in remote sensing: A survey,” Information Fusion, vol. 89, pp. 405–417, Jan. 2023.
21. G. Li, L. Sheng, B. Duan, Y. Li, D. Hei, and Q. Xing, “Unsupervised deep learning method for single image super-resolution of the thick
pinhole imaging system using deep image prior,” 2025.