[036] Kexin Shi, Jiangjun Peng*, Jing Gao, Yisi Luo, Shuang Xu* “Hyperspectral Image denoising via Double Subspace Deep Prior,” IEEE Trans. Geosci. Remote. Sens. (IEEE TGRS), vol. 62, art. no. 5531015, 2024. [Link] [Code] [Arxiv] [BibTeX]

[036] Shuang Xu, Jiangjun Peng, Teng-Yu Ji, Xiangyong Cao*, Kai Sun, Rongrong Fei and Deyu Meng “Stacked Tucker Decomposition with Multi-Nonlinear Products for Remote Sensing Imagery Inpainting,” IEEE Trans. Geosci. Remote. Sens. (IEEE TGRS), vol. 62, art. no. 5533413, 2024. [Link] [BibTeX]

[035] Shuang Xu, Qiao Ke, Jiangjun Peng*, Xiangyong Cao*, Zixiang Zhao “Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation,” IEEE Trans. Geosci. Remote. Sens. (IEEE TGRS), vol. 62, art. no. 5528714, 2024. [Link] [Code] [Arxiv] [BibTeX]

[034] Shuang Xu, Jilong Wang and Jialin Wang* “Fast Thick Cloud Removal for Multi-Temporal Remote Sensing Imagery via Representation Coefficient Total Variation,” Remote Sensing, vol. 16, art. no. 152, 2024. [Link] [Code] [BibTeX]

[033] Qiao Ke*, Xinhui Jing, Marcin Woźniak, Shuang Xu, Yunji Liang and Jiangbin Zheng “APGVAE: Adaptive disentangled representation learning with the graph-based structure information,” Informatin Sciences, vol. 657, Feb. 2024, art. no. 119903. [Link] [BibTeX]

[032] Kai Sun, Jiangshe Zhang, Shuang Xu*, Zixiang Zhao, Chunxia Zhang, Junmin Liu and Junying Hu “CACNN: Capsule Attention Convolutional Neural Networks for 3D Object Recognition,” IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3326606. [Link] [BibTeX]

[031] Kai Sun, Jiangshe Zhang, Jialin Wang, Shuang Xu*, Chunxia Zhang and Junying Hu “MBIAN: Multi-level bilateral interactive attention network for multi-modal image processing,” Expert Systems with Applications, vol. 231, art. no. 120733, 2023. [Link] [BibTeX]

[030] Kai Sun, Jiangshe Zhang, Junmin Liu, Shuang Xu*, Xiangyong Cao and Rongrong Fei “Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening,” Remote Sensing, vol. 15, art. no. 2869, 2023. [Link] [BibTeX]

[029] Yifan Wang, Shuang Xu*, Xiangyong Cao, Qiao Ke, Teng-Yu Ji and Xiangxiang Zhu “Hyperspectral Denoising Using Asymmetric Noise Modeling Deep Image Prior,” Remote Sensing, vol. 15, art. no. 1970, 2023. [Link] [BibTeX]

[028] Shuang Xu, Xiangyong Cao, Jiangjun Peng, Qiao Ke, Cong Ma and Deyu Meng, “Hyperspectral image denoising by asymmetric noise modeling,” IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), vol. 60, art. no. 5545214, 2022. [Link] [Code] [BibTeX]

[027] X Wei, C Zhang, H Wang, Z Zhao, D Xiong, S. Xu, J Zhang, SW Kim, “Hybrid Loss Guided Coarse-to-fine Model for Seismic Data Consecutively Missing Trace Reconstruction,” IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), vol. 60, art. no. 5923315, 2022. [Link] [BibTeX]

[026] C. Ma, J. Zhang, Z. Li, S. Xu, “Multi-Agent Deep Reinforcement Learning Algorithm with Trend Consistency Regularization for Portfolio Management,” Neural Computing and Applications, vol.35, no. 9, pp. 6589--6601, 2023. [Link] [BibTeX]

[025] F. Gao, J. Zhang, C. Zhang, S. Xu, C. Ma, “Long Short-Term Memory Networks with Multiple Variables for Stock Market Prediction,” Neural Processing Letters, vol. 44, no. 4, pp. 4211-4229, 2023. [Link] [BibTeX]

[024] S. Xu, J. Zhang, J. Wang, C. Zhang, “Hyperspectral Image Denoising by Low-Rank Models with Hyper-Laplacian Total Variation Prior,” Signal Processing, vol. 201, art. no. 108733, 2022. [Link] [BibTeX] [Code]

[023] Y. Yan, J. Liu, S. Xu, Y. Wang, X. Cao, “MD3Net: Integrating Model-driven and Data-driven Approaches for Pansharpening,” IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), vol. 60, art. no. 5411116, 2022. [Link] [BibTeX] [Code]

[022] S. Xu, J. Zhang, J. Wang, K. Sun, C. Zhang, J. Liu and J. Hu, “A model-driven network for guided image denoising,” Information Fusion, vol. 85, pp: 60-71, 2022. [Link] [BibTeX] [Code]

[021] Z. Zhao, S. Xu, J. Zhang, C. Liang, C. Zhang and J. Liu, “Efficient and Model-Based Infrared and Visible Image Fusion via Algorithm Unrolling,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp: 1186 - 1196, 2022. [Link] [Arxiv] [Code] [BibTeX]

[020] X. Wei, C. Zhang, S. Kim, K. Jing, Y. Wang, S. Xu and Z. Xie, “Seismic fault detection using convolutional neural networks with focal loss,” Computers & Geosciences, vol. 158, art. no. 104968, 2022. [Link] [BibTeX]

[019] L. Ji, J. Zhang, C. Zhang, C. Ma, S. Xu and K. Sun, “CondenseNet with exclusive lasso regularization,” Neural Computing and Applications (NCAA), vol. 33, pp. 16197-16212, 2021. [Link] [BibTeX]

[018] Y. Wang, S. Xu, J. Zhang, C. Zhang, Z. Zhao and J. Liu, “MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion,” Signal Processing: Image Communication (SPIC), vol. 96, art. no. 116295, 2021. [Link] [PDF] [Arxiv] [Code] [BibTeX]

[017] S. Xu, L. Ji, Z. Wang, P. Li, K. Sun, C. Zhang and J. Zhang, “Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy,” IEEE Transactions on Computational Imaging (IEEE TCI), vol. 6, pp. 1561-1570, 2020. [Link] [PDF] [Arxiv] [Code] [BibTeX]

[016] C. Zhou, J. Zhang, J. Liu, C. Zhang, R. Fei and S. Xu, “PercepPan: Towards Unsupervised Pan-Sharpening Based on Perceptual Loss,” Remote Sensing (RS), vol. 12, no. 14, art. no. 2318, 2020. [Link] [BibTeX]

[015] O. Amira, S. Xu, F. Du, J. Zhang, C. Zhang, and R. Hamza, “Weighted-Capsule routing via a fuzzy Gaussian model,” Pattern Recognition Letters (PRL), vol. 138, pp. 424-430, 2020. [Link] [PDF] [BibTeX]

[014] X. Yang, L. Tian, Y. Chen, L. Yang, S. Xu, and W. Wu, “Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition,” IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE/ACM TCBB), vol. 17, no. 4, pp. 1262 - 1275, 2020. [Link] [PDF] [Arxiv] [BibTeX]

[013] Z. Zhao, S. Xu, C. Zhang, J. Liu and J. Zhang, “Bayesian Fusion for Infrared and Visible Images,” Signal Processing (SP), vol. 177, art. no. 107734, 2020. [Link] [PDF] [Arxiv] [Code] [BibTeX]

[012] X. Huang, S. Xu, C. Zhang, and J. Zhang, “Robust CP Tensor Factorization With Skew Noise,” IEEE Signal Processing Letters (IEEE SPL), vol. 27, pp. 785-789, 2020. [Link] [PDF] [Code] [BibTeX]

[011] S. Xu, C. Zhang, and J. Zhang, “Adaptive Quantile Low-Rank Matrix Factorization,” Pattern Recognition (PR), vol. 103, art. no. 107310, 2020. [Link] [PDF] [Arxiv] [Code] [BibTeX]

[010] S. Xu, C. Zhang, P. Wang and J. Zhang, “Variational Bayesian weighted complex network reconstruction,” Information Sciences (Inf. Sci.), vol. 521, pp. pp. 291-306, June 2020. [Link] [PDF] [Arxiv] [Code] [BibTeX]

[009] S. Xu, O. Amira, J. Liu, C. Zhang, J. Zhang and G. Li, “HAM-MFN: Hyperspectral and Multispectral Image Multiscale Fusion Network With RAP Loss,” IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS), vol. 58, no. 7, pp. 4618-4628, 2020. [Link] [PDF] [BibTeX]

[008] S. Xu, C. Zhang and J. Zhang, “Bayesian deep matrix factorization network for multiple images denoising,” Neural Networks (NN), vol. 123, pp. 420-428, 2020. [Link] [PDF] [BibTeX]

[007] S. Xu and C. Zhang, “Robust sparse regression by modeling noise as a mixture of Gaussians,” Journal of Applied Statistics, vol. 46, no. 10, pp. 1738-1755, 2019. [Link] [PDF] [Code] [BibTeX]

[006] C. Zhang, S. Xu and J. Zhang, “A novel variational Bayesian method for variable selection in logistic regression models,” Computational Statistics & Data Analysis (CSDA), vol. 113, pp. 1-19, 2019. [Link] [PDF] [Code] [BibTeX]

[005] S. Xu, P. Wang and C. Zhang, “Identification of influential spreaders in bipartite networks: A singular value decomposition approach,” Physica A: Statistical Mechanics and its Applications, vol. 513, pp. 297–306, 2019. [Link] [PDF] [BibTeX]

[004] S. Xu, P. Wang, C. Zhang and J. Lü, “Spectral Learning Algorithm Reveals Propagation Capability of Complex Networks,” IEEE Transactions on Cybernetics (IEEE TCYB), vol. 49, no. 12, pp. 4253 - 4261, 2019. [Link] [PDF] [BibTeX]

[003] P. Wang and S. Xu, “Spectral coarse grained controllability of complex networks,” Physica A: Statistical Mechanics and its Applications, vol. 478, pp. 168-176, 2017. [Link] [PDF] [BibTeX]

[002] S. Xu, P. Wang and J. Lü, “Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks,” Scientific Reports, vol. 7, art. no. 41321, 2017. [Link] [PDF] [BibTeX]

[001] S. Xu and P. Wang, “Identifying important nodes by adaptive LeaderRank,” Physica A: Statistical Mechanics and its Applications, vol. 469, pp. 654-664, 2017. [Link] [PDF] [BibTeX]

[012] Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc Van Gool, “Equivariant multi-modality image fusion,” CVPR, 2024, pp. 25912-25921. [Link] [Code]

[011] Zixiang Zhao, Jiang-She Zhang, Haowen Bai, Yicheng Wang, Yukun Cui, Lilun Deng, Kai Sun, Chunxia Zhang, Junmin Liu, Shuang Xu, “Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion,” CVPR Workshops, 2023, pp. 2369-2377. [Link] [Code]

[010] Zixiang Zhao, Jiangshe Zhang, Xiang Gu, Chengli Tan, Shuang Xu, Yulun Zhang, Radu Timofte, Luc Van Gool, “Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution,” ICCV, 2023, pp. 12547-12558. [Link] [Code]

[009] Zixiang Zhao, Haowen Bai, Yuanzhi Zhu, Jiangshe Zhang, Shuang Xu, Yulun Zhang, Kai Zhang, Deyu Meng, Radu Timofte, Luc Van Gool, “DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion,” ICCV, 2023, pp. 8082-8093. [Link] [Code]

[008] Z Zhao, H Bai, J Zhang, Y Zhang, S. Xu, Z Lin, R Timofte, L Van Gool, “CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion,” CVPR, Vancouver, BC, Canada, June 17-24, 2023, pp. 5906-5916. [Link] [Code]

[007] Z. Zhao, J. Zhang, S. Xu, Z. Lin and H. Pfister, “Discrete Cosine Transform Network for Guided Depth Map Super-Resolution,” CVPR, New Orleans, LA, USA, June 18-24, 2022, pp. 5697-5707. (Oral) [Link] [Code]

[006] S. Xu, J. Zhang, Z. Zhao, K. Sun, L. Huang, J. Liu and C. Zhang, “Deep Gradient Projection Networks for Pan-sharpening,” CVPR, Virtual, June 19-25, 2021, pp. 1366-1375. (Poster) [Link] [Code]

[005] S. Xu, J. Zhang, Z. Zhao, K. Sun, L. Huang, J. Liu and C. Zhang, “Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information,” ICME, Virtual, July 5-9, 2021, pp. 1-6. (Poster) [Link] [Code]

[004] Z. Zhao, J. Zhang, S. Xu, K. Sun, L. Huang, J. Liu and C. Zhang, “FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter,” ICME, Virtual, July 5-9, 2021, pp. 1-6. (Oral) [Link] [Arxiv] [Code]

[003] Z. Zhang, C. Yu, S. Xu, H. Li, “Learning Flexibly Distributional Representation for Low-quality 3D Face Recognition,” AAAI, Virtual Event, February 2-9, 2021, pp. 3465-3473. (Poster) [Link]

[002] Z. Zhao, S. Xu (Equal Contribution), C. Zhang, J. Liu, J. Zhang, “DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion,” IJCAI, Yokohama, Japan, Jan. 7-15, 2021, pp. 970-976. (Poster) [Link] [PDF] [Arxiv] [Code]

[001] S. Xu and P. Wang, “Coarse graining of complex networks: A k-means clustering approach,” Chinese Control and Decision Conference (CCDC), Yinchuan, China, 28-30 May 2016, pp. 1948-9447. (Oral) [Link] [PDF]

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