Fei Zhu
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Publications

DNA Storage（DNA存储）
将数字信息编码为DNA序列并通过分子合成写入，随后利用测序和解码恢复原始信息。

1. H. Lin, Q. Shen, F. Zhu*, Z. Qin, L. Yang and Y. Duan. FedDNA: DNA Sequence Reconstruction via Deep Evidential Learning and Personalized Federated Aggregation,Proceedings of the 40th AAAI Conference on Artificial Intelligence (AAAI), vol. 40, no. 28, pp. 23523–23531, 2026. [link]
2. Q. Yun, F. Zhu*, B. Xi and Y. Duan, TransDNA: A Deep Transfer Learning Network for Sequence Reconstruction in DNA-Based Data Storage, IEEE Transactions on Computational Biology and Bioinformatics, pp. 1-12, 2025. [link]
3. Q. Yun, F. Zhu*, B. Xi, and L. Song. Robust Multi-read Reconstruction from Noisy Clusters Using Deep Neural Network for DNA Storage,Computational and Structural Biotechnology Journal, 23(2024):1076-1087. [link]
4.C. Dou, Y.Yang, F. Zhu, B. Li*, Y. Duan*, Explorer: efficient DNA coding by De Bruijn graph toward arbitrary local and global biochemical constraints, Briefings in Bioinformatics, Volume 25, Issue 5, September 2024. [link]

Hyperspectral Image Processing（高光谱图像处理）
利用丰富的连续光谱信息进行物质成分分析与场景识别，主要研究解混、全色锐化与分类。

1. Y. Wang, C. Wang, Y. Duan and F. Zhu*. HetDiff: Uncertainty-Guided Diffusion with Spectral Variability Modeling for Hyperspectral Pansharpening, Proceedings of the 34th ACM International Conference on Multimedia (ACM MM), 2026.
2. C. Wang, J. Gao, F. Zhu*, A. Halimi and C. Richard. DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, vol. 64, pp. 1-17, 2026. [link]
3. J. Gao, J. Shi and F. Zhu*, Robust Sparse Unmixing via Continuous Mixed Norm to Address Mixed Noise, IEEE Geoscience and Remote Sensing Letters., doi: 10.1109/LGRS.2025.3548697, 2025. [link]
4. T. Fang, F. Zhu* and J. Chen. Hyperspectral Unmixing Based on Multilinear Mixing Model Using Convolutional Autoencoders. IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024. [link]
5. A.J.X. Guo and F. Zhu*. Improving deep hyperspectral image classification performance with spectral unmixing, Signal Processing,vol. 183, 2021.[link]
6. Y. Zhang, F. Zhu*, A Kernel-based Weight Decorrelation for Regularizing CNNs, Neurocomputing,vol. 429, pp.47-59, 2021.[link]
7. X. Zhao, Y. Liang, A.J.X. Guo*, F. Zhu, Classification of small-scale hyperspectral images with multi-source deep transfer learning, Remote Sensing Letters, vol. 11, no. 4, pp. 303-312, 2020. [link]
8. M. Li, F. Zhu*, A.J.X. Guo and J. Chen, A Graph Regularized Multilinear Mixing Model for Nonlinear Hyperspectral Unmixing, Remote Sensing, 11(19),2188, 2019. [link]
9. Y. Liang, X. Zhao, A.J.X. Guo* and F. Zhu. Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field, IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 6, pp. 1042-1046, June 2020.
10. A.J.X. Guo and F. Zhu*. A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification,IEEE Transactions on Geoscience and Remote Sensing,57(9), pp.7170-7181, Sept.2019.[link]
11. A.J.X. Guo and F. Zhu*. Spectral-Spatial Feature Extraction and Classification by ANN Supervised With Center Loss in Hyperspectral Imagery,IEEE Transactions on Geoscience and Remote Sensing,57(3),pp.1755-1767, Mar.2019. [link]
12. F. Zhu, A. Halimi, P. Honeine*, B. Chen, and N. Zheng. Correntropy Maximization via ADMM: Application to Robust Hyperspectral Unmixing,IEEE Transactions on Geoscience and Remote Sensing,55(9),pp.4944-4955, Sept.2017. [link]
13. F. Zhu and P. Honeine*. Online kernel nonnegative matrix factorization, Signal Processing,131,143-153, Feb. 2017. [link]
14. F. Zhu and P. Honeine*. Bi-objective nonnegative matrix factorization Linear Versus Kernel-Based Models,IEEE Transactions on Geoscience and Remote Sensing,54(7),pp.4012-4022, Apr.2016. [link]
15. M. Li, F. Zhu* and A.J.X. Guo, A robust multilinear mixing model with L2,1 norm for unmixing hyperspectral images, IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020.
16. F. Zhu, P. Honeine, J. Chen Pixel-wise linear/nonlinear nonnegative matrix factorization for unmixing of hyperspectral data. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 4--8 May, 2020. [link]
17. F. Zhu, A. Halimi, P. Honeine, B. Chen, N. Zheng. ADMM for Maximum Correntropy Criterion. 2016 International Joint Conference on Neural Network, Vancouver, Canada, (IJCNN): 24--29 July, 2016. [link]
18. F. Zhu and P. Honeine. Pareto front of bi-objective kernel-based nonnegative matrix factorization. 23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Bruges, Belgium, 22--24 Apr. 2015.
19. F. Zhu, P. Honeine and K. Maya. Kernel non-negative matrix factorization without the pre-image problem. 24th IEEE workshop on Machine Learning for Signal Processing (MLSP). Reims, France,21--24 Sept. 2014. [link]

Patent

1. F. Zhu, Y. Qin. 一种基于深度迁移学习的多序列重建模型及方法, CN202410240081.6,2024.03.04.
2. B.Xi, F. Zhu, Y. Qin,Y. Duan. DNA存储中基于最大后验概率的鲁棒多序列重建, CN202310985626.1,2023.08.07.
3. Y. Qin, F. Zhu. DNA存储中一种对抗簇内噪声的多序列重建方法, CN202211190829.3,2022.09.28.
4. Y. Yang, C. Dou, F. Zhu, Y. Duan. 一种基于神经网络的DNA存储中恶意篡改检测方法, CN202211202184.0,2022.09.29.
5. F. Zhu, K. Teng, Y. Weng. DNA存储中满足生化约束的四进制旋转编码.
6. F. Zhu, T. Shen, Z. Yan. 一种DNA存储中无先验信息VT码迭代译码方法.