谭山，男，湖南娄底人。2007年3月毕业于西安电子科技大学，获模式识别与智能系统专业博士学位。2007年6月至2011年6月分别在美国休斯敦大学、马里兰大学从事博士后研究。2011年6月回国工作。谭山博士主要从事图像处理、生物医学成像、智能医疗等领域的研究。曾获全国百优博士论文提名奖（教育部、国务院学位委员会，2009）、全国高等学校科学研究优秀成果奖（自然科学一等奖，教育部，2009）、陕西高等学校科学技术奖（一等奖，陕西教育厅，2009）、美国医学物理师协会年会最佳论文奖(AAPM Best Paper in Physics, 2012)，IOP Publishing Top Cited Paper Award (英国物理学会出版社，2021)，模式识别国际会议最佳论文奖 (ICPR Best Scientific Paper Award，2022)。谭山博士课题组提出的Hessian结构光超分辨荧光显微成像算法入选“2018年度中国光学十大进展”。

讲授课程：

   数字图像处理、医学图像处理、人工智能进展、科学研究方法导论、文献检索与科技论文写作

主持的部分项目:

[1]. 基于扩散先验的真实场景贝叶斯图像复原，国家自然科学基金，面上项目，2025.1-2028.12，主持

[2]. 高分辨荧光显微成像的信号恢复理论与算法研究, 国家自然科学基金，面上项目，2021.1-2024.12，主持；

[3]. 锥形束CT图像重构中的最优多重正则理论及算法研究, 国家自然科学基金，面上项目，2017.1-2020.12，主持；

[4]. PET图像盲分割问题的理论和算法研究, 国家自然科学基金，面上项目，2014.1-2017.12，主持；

招生信息：

欢迎热爱学术研究且数学基础较好的同学报考博士或硕士研究生，本科专业不限。同时接收本科生（理、工类）进行科研培训。有意者请Email联系。

部分科研成果：

[1]. 脊波双框架系统与自然图像的多变量统计模型，全国优秀博士学位论文提名论文奖(2009)

[2]. 智能图像理解的基础理论与方法研究，全国高等学校科学研究优秀成果奖自然科学一等奖(2009)

[3]. Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemoradiotherapy Using Spatial-Temporal FDG-PET features, Clinical Parameters and Demographics，the 54th Annual Meeting of the AAPM, Best in Physics, 2012.

[4]. 超快、长时程超分辨率海森结构光照明显微镜，中国光学十大进展(2018年)

[5]. Tumor Co-Segmentation in PET/CT using Multi-Modality Fully Convolutional Neural Network, IOP Publishing Top Cited Paper Award (2021)

[6]. Decoupled Frequency Learning for Dynamic Scene Deblurring，ICPR Best Scientific Paper Award (2022)

部分已发表文献:

[1]. J. Liu, X. Dong, H. Lu, T. Liu, W. Liu, X. Hu, Q. Meng, A. Jiang, T. Jiang, X. Geng, H. Liu, J. Cheng, E. Y. Lam, Y.-J. Liu*, S. Tan*, and D. Li*, "Bio-friendly and High-precision Super-resolution Imaging through Self-supervised Reconstruction Structured Illumination Microscopy," Nature Methods, vol. 23, pp. 395-404, 2026. (专利由华中科技大学持有)

[2]. T. Liu, J. Liu, D. Li*, and S. Tan*, "Bayesian Deep-learning Structured Illumination Microscopy Enables Reliable Super-resolution Imaging with Uncertainty Quantification," Nature Communications, vol. 16, p. 5027, 2025. (专利由华中科技大学持有)

[3]. Z. Peng, S. Kang, X. Huang, X. Xiang, G. He, T. Liu, W. Mei*, and S. Tan*, "HA-SAM: Hierarchically Adapting SAM for Nerve Segmentation in Ultrasound Images," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2025, pp. 322-331.

[4]. Y. Lan, C. xin, J. Cheng, and S. Tan, "Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition," Transactions on Machine Learning Research, August 2025. (华科本科生一作)

[5]. J. Cheng and S. Tan*, "Diffusion Priors for Variational Likelihood Estimation and Image Denoising," in The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024, pp. 61138-61159.

[6]. X. Liu, Y. Xie, S. Diao, S. Tan*, and X. Liang*, "Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains," IEEE Transactions on Medical Imaging, vol. 43, pp. 3533-3545, 2024.

[7]. J. Cheng, D. Liang, and S. Tan*, "Transfer CLIP for Generalizable Image Denoising," in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25974-25984.

[8]. T. Liu, J. Cheng, and S. Tan*, "Spectral Bayesian Uncertainty for Image Super-Resolution," in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 18166-18175.

[9]. J. Cheng, T. Liu, and S. Tan*, "Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising," in 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 12891-12902.

[10]. T. Liu, J. Liu, D. Li, and S. Tan*, "Improving Reconstruction of Structured Illumination Microscopy Images via Dual-Domain Learning," IEEE Journal of Selected Topics in Quantum Electronics, vol. 29, pp. 1-12, 2023.

[11]. T. Liu and S. Tan*, "Decoupled Frequency Learning for Dynamic Scene Deblurring," in 2022 26th International Conference on Pattern Recognition (ICPR), 2022, pp. 89-96. (Best Scientific Paper Award)

[12]. H. Liu, L. Li, J. Lu, and S. Tan*, "Group Sparsity Mixture Model and Its Application on Image Denoising," IEEE Transactions on Image Processing, vol. 31, pp. 5677-5690, 2022.

[13]. H. Liu, X. Liu, J. Lu, and S. Tan*, "Self-Supervised Image Prior Learning with GMM from a Single Noisy Image," in IEEE/CVF International Conference on Computer Vision (ICCV), 2021.

[14]. J. Liu, X. Huang, L. Chen, and S. Tan*, "Deep learning–enhanced fluorescence microscopy via degeneration decoupling," Optics Express, vol. 28, pp. 14859-14873, 2020/05/11 2020.

[15]. X. Zhao, L. Li, W. Lu, and S. Tan*, "Tumor Co-Segmentation in PET/CT using Multi-Modality Fully Convolutional Neural Network," Physics in medicine and biology, vol. 64, p. 015011, 2019. (IOP Publishing Top Cited Paper Award)

[16]. H. Liu and S. Tan*, "Image Regularizations Based on the Sparsity of Corner Points," IEEE Trans Image Process, vol. 28, pp. 72-87, Jan 2019.

[17]. X. Huang, J. Fan, L. Li, H. Liu, R. Wu, Y. Wu, L. Wei, H. Mao, A. Lal, P. Xi, L. Tang, Y. Zhang, Y. Liu, S. Tan*, L. Chen*, "Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy," Nature Biotechnology, vol. 36, p. 451, 04/11/online 2018. (入选2018年中国光学十大进展。专利由华中科技大学持有)

[18]. B. Chen, K. Xiang, Z. Gong, J. Wang*, and S. Tan*, "Statistical Iterative CBCT Reconstruction Based on Neural Network," IEEE Trans. Medical Imaging, vol. 37, pp. 1511-1521, Jun 2018.

[19]. L. Liu, X. Li, K. Xiang, J. Wang, and S. Tan*, "Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties," IEEE Transactions on Medical Imaging, vol. 36, pp. 2588-2599, 2017.

  [20]. T. Sun, N. Sun, J. Wang, and S. Tan*, "Iterative CBCT reconstruction using Hessian penalty," Physics in medicine and biology, vol. 60, pp. 1965-1987, Feb 12 2015. (华科本科生一作)