姓名： 宋巍 导师类别： 博导

所在专业： 海洋科学，软件工程 研究方向： 计算机视觉、深度学习、图像/视频分析、海洋大数据
学院/单位： 信息学院
专业所属学院： 信息学院
专业技术职务： 教授 行政职务： 无
性别： 女 E-Mail： wsong@shou.edu.cn
最高学位： 博士
通讯地址：
备注：
简历

宋巍，上海海洋大学教授，硕士生导师、博士生导师，2016年上海市高校****“****”。
主要研究方向包括海洋大数据、计算机视觉、图像和视频分析技术等。2016年以来致力于海洋信息交叉学科研究，基于计算机视觉、深度学习等技术开展海洋要素分析、海冰识别、水下/深海机器视觉增强等前沿研究。
主持国家自然科学基金2项，主持上海市科委项目1项，参与多项国家及行业重大专项。在国内外期刊和会议上发表学术论文50多篇，包括：IEEE Trans. Broadcasting, Information Fusion、模式识别与人工智能、中国图象图形学报（获2019年优秀论文）、中国计算机协会CCF推荐的A、B类国际会议等；2016年11月与荷兰、澳大利亚、意大利、英国等大学教授共同创建智能感知国际合作实验室，在此基础上开展了广泛国际合作研究并发表多篇国际合作论文。
课题组有优越的科研环境和科研平台，欢迎敏而好学、勤奋上进、勇于挑战、积极乐观的同学加入我们。
无论你是打算硕士后继续深造，还是希望提高动手研发能力提升工作竞争力，这里都是适合你的土壤。三年后期待一个更加优秀的你！

教育经历

09/2008 – 10/2012博士(Ph.D)
昆士兰科技大学 (Queensland University of Technology, Brisbane, Australia)


工作经历

2016.1- 至今 上海海洋大学 信息学院 教授
2012 - 2015 澳大利亚昆士兰科技大学 研究员

研究成果

2019.11 极端环境下海洋科考数据保障与智能服务关键技术 上海市浦东新区科技进步奖 二等奖
2017.10 基于全生命周期的海洋灾害大数据智能服务平台中国产学研合作创新成果一等奖

获奖情况

2019.12上海海洋大学2019年优秀研究生导师
2019 卷积神经网络和深度置信网络在SAR影像冰水分类的性能评估 《中国图象图形学报》2019年度优秀论文

专利著作

Book:
1. Huang, D., Song, W., & Zou, G., (2019). Marine Big Data, World Scientific Publishing, Singapore.
Book Chapters:
1. Song, W.,Tjondronegoro, D., and Docherty, M. (2013). User-centered study on quality of mobile video services. In Tjondronegoro, D. (Eds.), Tools for Mobile Multimedia Programming and Development. IGI Global.
2. Song, W., Tjondronegoro, D., and Docherty, M. (2012). Understanding user experience of mobile video: Framework, measurement, and optimisation. In Tjondronegoro, D. (Eds.), Mobile Multimedia – User &Technology Perspectives. 3-30, Jan 2012, InTech Open Access. (top 3 most downloaded chapter)
专利：
一种基于相对全局直方图拉伸的浅海水下图像增强方法，授权号：20**

论文发表

Journals:
1. He, Q., Zha, C., Song, W.*, Hao, Z., Du, Y., Liotta, A., & Perra, C. (2020). Improved Particle Swarm Optimization for Sea Surface Temperature Prediction. Energies, 13(6), 1369.
2. Song, W., Wang, Y., Huang, D., Liotta, A., & Perra, C. (2019). Enhancement of Underwater Images With Statistical Model of BackgroundLight and Optimization of Transmission Map. IEEE Transactions on Broadcasting. doi: 10.1109/TBC.2019.**
3. Wang, Y., Song, W.*, Fortino, G., Qi, L., Zhang, W., & Liotta, A. (2019). An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2019.**
4. Du, Y., Liu, J., Song, W., He, Q., & Huang, D. (2019). Ocean Eddy Recognition in SAR Images With Adaptive Weighted Feature Fusion. IEEE Access, 7, 152023–152033. https://doi.org/10.1109/ACCESS.2019.**
5. Du, Y., Song, W.*, He, Q., Huang, D., Liotta, A., & Su, C. (2019). Deep learning with multi-scale feature fusion in remote sensing for automatic oceanic eddy detection. Information Fusion, 49, 89–99. https://doi.org/10.1016/j.inffus.2018.09.006
6. Ma, Z*.,Song, W*., Deng, J., Wang, J., Cui, C. A Rational Function Model Based Geo-Positioning Method for Satellite Images without Using Ground Control Points.Remote Sens. 2018, 10, 182.
7. Yang, M.,Song, W.,& Mei, H. (2017). Efficient retrieval of massive ocean remote sensing images via a cloud-based mean-shift algorithm.Sensors, 17(7). 1693.
8. Himawan, I.,Song, W., & Tjondronegoro, D. (2015) Impact of automatic region-of-interest coding on perceived quality in mobile video.Multimedia Tools and Applications. 76: 785-813.
9. Song, W.,and Tjondronegoro, D. (2014). Acceptability-based QoE models for mobile video.IEEE Transactions on Multimedia, 16(3):738-750
Referred Conference Papers:
1. Corda, R., Song, W., Liotta, A., & Perra, C. (2020). A comparison of hologram plane coding and object plane coding on different types of holograms. In P. Schelkens & T. Kozacki (Eds.), Optics, Photonics and Digital Technologies for Imaging Applications VI (p. 34). SPIE. https://doi.org/10.1117/12.**
2. Song, W, Dai, S.Y., Huang, D.M., Wang, J., Liotta, A., & Di Fatta, G. (2019). Bi-gradient Verification for Grad-CAM towards Accurate Visual Explanation for Remote Sensing Images. 2019 IEEE International Conference on Data Mining Workshops (ICDMW).
3. Song, W., Li, M., He, Q., Huang, D., Perra, C., & Liotta, A. (2018). A Residual Convolution Neural Network for Sea Ice Classification with Sentinel-1 SAR Imagery. 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 795–802. https://doi.org/10.1109/ICDMW.2018.00119
4. Song, W., Wang, Y., Huang, D., & Tjondronegoro, D. (2018). A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration. In R. Hong, W.-H. Cheng, T. Yamasaki, M. Wang, & C.-W. Ngo (Eds.), Advances in Multimedia Information Processing – PCM 2018 (Vol. 11164, pp. 678–688). https://doi.org/10.1007/978-3-030-00776-8_62
5. Petra C., Song, W., Liotta A., (2018)Effects of light field subsampling on the quality of experience in refocusing applications. QoMex 2018.
6. Huang D., Wang Y.,Song W*., Sequeira J., Mavromatis S. (2018)Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition. In: Schoeffmann K. et al. (eds) MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science, vol 10704. Springer, Cham
7. Huang, D., Du, Y., He, Q.,Song, W*.,& Liotta, A. (2017).DeepEddy: A simple deep architecture for mesoscale oceanic eddy detection in SAR images. IEEE, International Conference on Networking, Sensing and Control (pp.673-678). IEEE.
8. Song, W.,Xiao, Y., Tjondronegoro, D. and Liotta, A. (2015).QoE Modeling for VP9 and H.265 Videos on Mobile Devices. Proc. of the 23th ACM Multimedia. Brisbane, Australia. pp.501-510
9. Song, W.,Tjondronegoro, D., and Himawan, I. (2014).Acceptability-based QoE Management for user-centric Mobile Video Delivery: A Field Study Evaluation. Proc. of the 22th ACM Multimedia. FL, USA. pp.267-276