基本信息

姓名：王立志

职称：教授、博士生导师

办公室地址：昌平校区人工智能学院524

电子邮箱：wanglizhi@bnu.edu.cn

                     

个人简介

王立志，教授、博士生导师、国家优青。西安电子科技大学工学学士，西电-微软亚研院联合培养博士。主持多项国家级项目，包括国自然优青项目、国自然联合重点项目、应用创新装备预研项目等，主持多项企业委托项目，来自华为、安克、兵器系统、航天系统等。发表40+篇CCF A类论文，包括10+篇TPAMI，担任IEEE TIP Associate Editor和CAD学报编委。荣获/入选斯坦福大学“全球前2%顶尖科学家”榜单、中国图象图形学学会自然科学一等奖、CCF A类会议ACM MM 2022最佳论文提名奖、中国电子学会2018年度优秀博士学位论文奖、IEEE VCIP 2016最佳论文奖、北京市优秀本科毕业设计指导教师。

研究领域

智能图像处理、计算摄像学、计算机视觉、智能语音处理

教育背景

2011年—2016年 西安电子科技大学-微软亚洲研究院 智能信息处理 博士 导师：石光明教授，吴枫教授

2007年—2011年 西安电子科技大学大学电子信息工程学院，探测制导与控制技术，学士

主持和参加的科研项目

国家自然科学基金联合重点项目

国家优秀青年科学基金项目

国家自然科学基金面上项目

国家自然科学基金青年项目

应用创新装备预研项目

华为难题揭榜项目

奖励与荣誉

中国图象图形学学会自然科学一等奖

CCF A类会议ACM MM 2022最佳论文提名奖（通讯作者）

中国电子学会2018年度优秀博士学位论文奖

IEEE VCIP 2016最佳论文奖（第一作者）

北京市优秀本科毕业设计指导教师

IEEE ICASSP 2024 光谱视觉挑战赛冠军

学术代表成果

1. Hansen Feng, Lizhi Wang*, Yiqi Huang, Yuzhi Wang, Lin Zhu, and Hua Huang. "Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising." IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026. (CCF A类).

2. Jie Lian, Lizhi Wang*, Lin Zhu, Renwei Dian, Zhiwei Xiong, and Hua Huang. "Building Non-Uniform Degradation Model for Position-Aware Hyperspectral Image Fusion." IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025. (CCF A类).

3. Yuchen Wang, Hongyuan Wang, Lizhi Wang*, Xin Wang, Lin Zhu, Wanxuan Lu, and Hua Huang. "Complementary advantages: Exploiting cross-field frequency correlation for nir-assisted image denoising." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (CCF A类).

4. Tong Li, Lizhi Wang*, Zhiyuan Xu, Lin Zhu, Wanxuan Lu, and Hua Huang. "Positive2negative: Breaking the information-lossy barrier in self-supervised single image denoising." In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (CCF A类).

5. Tong Li, Hansen Feng, Lizhi Wang*, Lin Zhu, Zhiwei Xiong, Hua Huang, Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. (CCF A类)

6. Lizhi Wang, Lingen Li, Weitao Song, Lei Zhang, Zhiwei Xiong, Hua Huang, Non-serial Quantization-aware Deep Optics for Snapshot Hyperspectral Imaging, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024. (CCF A类)

7. Xin Wang, Lizhi Wang*, Xiangtian Ma, Maoqing Zhang, Lin Zhu, Hua Huang, In2SET: Intra-Inter Similarity Exploiting Transformer for Dual-Camera Compressive Hyperspectral Imaging, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. (CCF A类)

8.  Hansen Feng, Lizhi Wang*, Yuzhi Wang, Haoqiang Fan, Hua Huang, Learnability Enhancement for Low-Light Raw Image Denoising: A Data Perspective, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. (CCF A类)

9. Jie Lian, Lizhi Wang*, He Sun, Hua Huang, GT-HAD: Gated Transformer for Hyperspectral Anomaly Detection, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.

10. Hongyuan Wang, Lizhi Wang*, Chang Chen, Xue Hu, Fenglong Song, Hua Huang, Learning Spectral-wise Correlation for Spectral Super-Resolution: Where Similarity Meets Particularity, ACM MM, 2023. (CCF A类)

11. Lingen Li, Lizhi Wang*, Weitao Song, Lei Zhang, Zhiwei Xiong, Hua Huang, Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (CCF A类)

12. Hansen Feng, Lizhi Wang*, Yuzhi Wang, Hua Huang, Learnability enhancement for low-light raw denoising: Where paired real data meets noise modeling, ACM MM,2022. (CCF A类) (Best Paper Runner-Up Award)

13. Lizhi Wang, Shipeng Zhang, Hua Huang, Adaptive Dimension-discriminative Low-rank Tensor Recovery for Computational Hyperspectral Imaging, International Journal of Computer Vision (IJCV), 2021. (CCF A类)

14. Shipeng Zhang, Lizhi Wang*, Lei Zhang, Hua Huang, Learning Tensor Low-Rank Prior for Hyperspectral Image Reconstruction, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (CCF A类)

15. Lizhi Wang, Chen Sun, Maoqing Zhang, Ying Fu, and Hua Huang. DNU: Deep non-local unrolling for computational spectral imaging. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (CCF A类)

16. Lizhi Wang, Zhiwei Xiong, Hua Huang, Guangming Shi, Feng Wu, and Wenjun Zeng. High-speed hyperspectral video acquisition by combining nyquist and compressive sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019. (CCF A类)

17. Lizhi Wang, Tao Zhang, Ying Fu, and Hua Huang. HyperReconNet: Joint coded aperture optimization and image reconstruction for compressive hyperspectral imaging. IEEE Transactions on Image Processing (TIP), 2019. (CCF A类)

18. Lizhi Wang, Chen Sun, Ying Fu, Min H Kim, and Hua Huang. Hyperspectral image reconstruction using a deep spatial-spectral prior. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (CCF A类)

19. Shipeng Zhang, Lizhi Wang*, Ying Fu, Xiaoming Zhong, and Hua Huang. Computational hyperspectral imaging based on dimension-discriminative low-rank tensor recovery. In IEEE International Conference on Computer Vision (ICCV), 2019. (CCF A类)

20. Lizhi Wang, Zhiwei Xiong, Guangming Shi, Feng Wu, and Wenjun Zeng. Adaptive nonlocal sparse representation for dual-camera compressive hyperspectral imaging. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017. (CCF A类)

21. Zhiwei Xiong, Lizhi Wang, Huiqun Li, Dong Liu, and Feng Wu. Snapshot hyperspectral light field imaging. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. (CCF A类)

22. Lizhi Wang, Zhiwei Xiong, Guangming Shi, Wenjun Zeng, and Feng Wu. Compressive hyperspectral imaging with complementary RGB measurements. In Visual Communications and Image Processing (VCIP), 2016. (Best Paper Award)

23. Lizhi Wang, Zhiwei Xiong, Dahua Gao, Guangming Shi, Wenjun Zeng, and Feng Wu. High-speed hyperspectral video acquisition with a dual-camera architecture. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. (CCF A类)

招生说明

团队有充足的博士生指标和硕士生指标，本人每年招收博士生1名、硕士生2-3名，长期招收科研入门的本科生，以及有志于教师岗位的博士后（留校后备）。本人会亲自指导每一位学生，团队的其他老师和高年级同学也会提供指导。团队会提供优越的工作环境、计算资源、科研补助和国内外交流机会。请有兴趣加入团队的同学尽早联系，早日确定意向。详细招生理念请转阅团队主页https://vmcl.bnu.edu.cn/