石武祯，博士，深圳大学电子与信息工程学院助理教授。于2020年4月获哈尔滨工业大学工学博士学位，于2020年6月加入丁文华院士领导的广东省数字创意技术工程实验室，任职助理教授。参与完成多项国家级科研项目，在图像处理、计算机视觉、图像/视频编码与传输等领域有较好的研究基础。已发表学术论文20余篇，其中代表性工作成果发表在IEEE Trans. Image Processing、IEEE Trans. CSVT和IEEE Trans. Multimedia等顶级国际期刊以及CVPR和DCC等顶级国际会议上，其中入选ESI高被引论文1篇。担任领域内十多个国际期刊审稿人，包括IEEE TIP、IEEE TCSVT、IEEE TII、IEEE JSTSP等。
办公室：深圳大学沧海校区致真楼L6-906
Email: wzhshi@szu.edu.cn
研究兴趣：计算机视觉、图像处理、图像/视频编码与传输
欢迎在数学、编程、英语等方面有特长并且对科研有热情的高年级本科生和研究生加入我的研究团队。
工作经历：
2020.06-至今，深圳大学，电子与信息工程学院，助理教授
教学信息：
数据结构（秋季学期，大二）、数字图像处理（春季学期，大三）
科研项目：
l 融合编码压缩先验的视频增强方法，广东省自然科学基金面上项目，在研，主持。
l 基于深度学习的图像和视频压缩感知，深圳市高等院校稳定支持计划面上项目，在研，主持。
部分代表性论文：
[1] Zhibo Liang, Shaohui Liu, Wuzhen Shi, Xingtao Wang, Feng Jiang. Small Object Recognition using a Spatio-temporal neural network. IEEE International Conference on Multimedia and Expo 2021. (CCF B, EI, 多媒体领域国际旗舰会议)
[2] Sabrina Narimene Benassou, Wuzhen Shi, Feng Jiang. Entropy Guided Adversarial Model for Weakly Supervised Object Localization. Neurocomputing, 2020, DOI: https://doi.org/10.1016/j.neucom.2020.11.006. (CCF B, Q1, IF: 4.438)
[3] Donghao Gu, Yaowei Li, Feng Jiang, Zhaojing Wen, Shaohui Liu, Wuzhen Shi, Guangming Lu, Changsheng Zhou. VINet: A Visually Interpretable Image Diagnosis Network. IEEE Transactions on Multimedia, vol. 22, no. 7, pp. 1720-1729, July 2020, doi: 10.1109/TMM.2020.**. (CCF B, JCR Q1, IF: 6.051, 多媒体领域顶级国际期刊)
[4] Wuzhen Shi, Feng Jiang, Shaohui Liu, Debin Zhao. Image compressed sensing using convolutional neural network. IEEE Transactions on Image Processing. 2020, 29, 375-388. DOI: 10.1109/ TIP.2019.** (入选ESI高被引论文，CCF A, JCR Q1, IF=9.34, 计算机视觉及人工智能领域顶级国际期刊)
[5] Wuzhen Shi, Feng Jiang, Shaohui Liu, Debin Zhao. Scalable convolutional neural networks for image compressed sensing. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 12282-12291, doi: 10.1109/CVPR.2019.01257. (CCF A, EI, 计算机视觉及人工智能领域顶级国际会议)
[6] Wuzhen Shi, Shaohui Liu, Feng Jiang, Debin Zhao. Video compressed sensing using a convolutional neural network. IEEE Transactions on Circuits and Systems for Video Technology, vol.31, no.2, pp. 425-438, Feb. 2021, doi: 10.1109/TCSVT.2020.**. (CCF B, JCR Q1, IF=4.133,视频压缩领域顶级国际期刊)
[7] Wuzhen Shi, Feng Jiang, Shengping Zhang, et al.. Hierarchical residual learning for image denoising. Signal Processing: Image Communication. 2019, 76, 243-251. (CCF C, JCR Q1, IF=2.814).
[8] Wuzhen Shi, Feng Jiang, Shaohui Liu, Debin Zhao. Multi-Scale Deep Networks for Image Compressed Sensing. IEEE International Conference on Image Processing 2018. (CCF C, EI, 图像处理国际旗舰会议)
[9] Wuzhen Shi, Shaohui Liu, et al.. Anchored Neighborhood Deep Network for Single Image Super-Resolution. EURASIP Journal on Image and Video Processing, 2018, 2018 (1):34. (JCR Q2, IF=1.534)
[10] Wuzhen Shi, Feng Jiang, Debin Zhao. Single Image Super-Resolution with Dilated Convolution based Multi-scale Information Learning Inception Module. IEEE International Conference on Image Processing 2017. (CCF C, EI，图像处理国际旗舰会议)
[11] Wuzhen Shi, Feng Jiang, Debin Zhao. Deep Networks for Compressed Image Sensing. IEEE International Conference on Multimedia and Expo 2017. (CCF B, EI, 多媒体领域国际旗舰会议)
[12] Wuzhen Shi, Feng Jiang, Debin Zhao. Image Entropy of Primitive and Visual Quality Assessment. IEEE International Conference on Image Processing 2016. (CCF C, EI, 图像处理国际旗舰会议)
[13] Wuzhen Shi, Congcong Chen, et al.. Group-based Sparse Representation for Low Lighting Image Enhancement. IEEE International Conference on Image Processing 2016. (CCF C, EI, 图像处理国际旗舰会议)
[14] Yang Wen, Ying Li, Xiaohua Zhang, Wuzhen Shi, et al.. A Weighted Full-Reference Image Quality Assessment Based on Visual Saliency[J]. Journal of Visual Communication and Image Representation, 2017, 43:119-126. (CCF C, JCR Q1, IF=2.479)
[15] Feng Jiang, Jie Ren, Changhoon Lee, Wuzhen Shi, et al.. Spatial and temporal pyramid-based real-time gesture recognition. Journal of Real-Time Image Processing, 2016. (JCR Q2, IF=1.968)
[16] Wen Tao, Feng Jiang, Shengping Zhang, Jie Ren, Wuzhen Shi, et al.. An End-to-End Compression Framework Based on Convolutional Neural Networks. Data Compression Conference 2017. (CCF B, EI，数据压缩领域顶级国际会议)