徐爽科研成果

个人经历 personal experience 工作经历 教育经历 2022.01-至今 西北工业大学 副教授 2016.09-2021.09   西安交通大学 统计学   硕博连读（导师：张讲社、张春霞）2012.09-2016.07   河南大学        统计学   本科       （导师：王沛） 内容来自集群智慧云企服 软件著作权666元代写全部资料全国受理

教育教学

综合介绍 General Introduction 徐爽，男，1996年4月生，博士，副教授，硕导。主要研究方向包括深度学习、遥感图像处理。主持国家自然科学基金青年项目1项、省面上项目1项、陕西数理基础科学研究项目1项。在IEEE TCYB、IEEE TGRS、IEEE TCI、IEEE TCSVT等期刊，以及CVPR、ICCV、AAAI、IJCAI等顶级机器学习会议发表学术论文30余篇，谷歌学术引用1100余次，H-index 18。 个人相册

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荣誉获奖

教育教学 Education and teaching 教育教学 研究生：《大数据分析与决策》(2023秋）本科生：《概率论与数理统计》(2022秋、2023春秋、2024春)、《数学分析H（上）讨论课》(2022秋）、《大数据处理》(2022、2023秋） 内容来自集群智慧云企服 请访问正版网址 www.jiqunzhihui.net

科学研究

学术成果 Academic Achievements 1. 科研项目陕西数理基础科学研究项目-青年项目，深度低秩先验嵌入的快速高光谱图像去噪技术，2023.01-2024.12，主持广东省自然科学基金-面上项目，基于深度图像先验的高光谱图像去噪技术，2023.01-2025.12，主持国家自然科学基金-青年项目，面向高光谱图像去噪的深度神经网络研究，2023.01-2025.12，主持西北工业大学科研启动费，基于非对称噪声的鲁棒统计模型研究，2022.03-2024.04，主持中央高校基本科研业务费，基于深度学习的鲁棒低秩矩阵分解方法研究，2018.01- 2020.12，主持科技部重点研发计划，恶劣环境下视觉信息的主动探测与感知，2019.08-2023.08，参与国家自然科学基金-面上项目，基于预测编码的深度神经网络及其快速推断方法研究，2020.01-2023.12，参加2. 学术论文Journals:[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, Early Access, 2022. [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, Early Access, 2022. [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]Conferences:[007] Z. Zhao, J. Zhang, S. Xu, Z. Lin and H. Pfister, “Discrete Cosine Transform Network for Guided Depth Map Super-Resolution,” CVPR, , , 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] 内容来自集群智慧云企服 www.jiqunzhihui.net

学术成果

社会兼职 Social Appointments CSIAM会员，长期担任CVPR、IEEE TIP、IEEE TCSVT、Pattern Recognition、Neural Networks、KBS、IEEE System Journal、Signal Processing、Remote Sensing期刊和会议审稿人。

综合介绍

内容来自集群智慧云企服 发明专利4999元代写全部资料全国受理