周冬明

教授
  • 博士，教授，博士生导师。
  • 联系方式：zhoudm@ynu.edu.cn
  • 地址：信息学院楼1312室

个人简介

受教育经历

2001/09—2004/06，复旦大学，信息学院电子工程系，博士。

1985/09—1988/06，华中理工大学（华中科技大学），自动控制工程系，硕士。

1981/09—1985/06，华中理工大学（华中科技大学），自动控制工程系，学士。

研究工作经历

2005/12—目前，云南大学，信息学院通信工程系，教授。

2008/03—2008/09，加拿大约克大学，访问学者。

1999/11—2005/11，云南大学，信息与电子科学系，副教授。

1990/11—1999/10，云南大学，信息与电子科学系，讲师。

1988/06—1990/10，云南大学，信息与电子科学系，助教


News!!!

祝贺：

2020级研究生杨绍良、赵倩获2022年度国家奖学金，2020级研究生杨浩获2022年度云南省政府奖学金

2019级研究生李淼荣升同济大学电子与工程学院博士生，2019级研究生夏伟代荣升中南大学计算机科学与技术博士生

2019级研究生李淼、臧永盛获2021年度国家奖学金，2019级研究生卫依雪获2021年度云南省政府奖学金

2018级研究生王长城、谢诗冬荣升云南大学信息学院博士生。

2020级博士生刘琰煜获2020年度云南省政府奖学金。

2019级博士生郭延哺获2019年度云南省政府奖学金；2020年度国家奖学金。

2018级研究生王长城获2020年度云南省政府奖学金。

2017级研究生刘琰煜、丁斋生荣升云南大学信息学院博士生，并获2019年度硕士云南省政府奖学金。 

2016级研究生侯瑞超荣升南京大学计算机系博士生。

2016级研究生侯瑞超、刘栋荣获2018年度硕士国家奖学金。

2015级研究生于传波荣升天津大学电气自动化与信息工程学院信息与通信工程博士生。

2013级研究生金鑫荣升云南大学信息学院博士生，并获得博士学术新人奖与博士生国家奖学金。

2014级研究生王佺、贺康建荣获硕士国家奖学金。

2014级研究生贺康建荣升云南大学信息学院博士生。

2014级研究生王佺荣升上海大学博士生。


科研

（一）研究方向：基于深度学习图像处理；基于机器学习的生物信息处理；神经网络的动力学机制研究；基于视觉皮层神经元模型的路径优化计算。

（二）研究成果：主持参与的研究项目：5个国家自然科学基金项目：复杂疾病的基因调控网络建模技术研究与应用（62066047），2021.01-2024.12，主持；面向多源图像融合贡献估计的多源脉冲信息交换编码与分数阶变分方法研究（61966037），2020.01-2023.12，参加；视觉皮层神经元模型的脉冲同步振荡相关理论及应用研究（61065008），2011.01-2013.12，主持，已结题；视觉皮层神经元脉冲同步振荡信息的图像融合技术研究（61365001），2014.01-2017-12，主持，已结题；视感知模型脉冲耦合神经网络的图像特征提取及应用研究（61463052），2015.01-2018-12，参加，已结题。4个省级项目：脉冲耦合神经网络动力学机制、目标识别和学习系统研究（省自然科学基金项目，2005F0010M），主持，已结题；脉冲耦合神经网络的图像处理及目标识别系统研究（省自然科学基金项目，2007F174M），主持，已结题；视感知模型PCNN的图像特征提取与应用研究（省自然科学基金青年项目，2012FD003），参加，已结题；视觉皮层模型的视感知模型理论与应用研究（省教育厅项目，2010Y247），参加，已结题。2个校级项目：脉冲耦合神经网络动力学机制、目标识别和学习系统研究（校级重点项目，2004Z007C），主持，已结题；脉冲耦合神经网络的动力学参数估计研究（校级青年项目，2007Q024C），参加，已结题。发表论文100余篇，申请发明专利7项，云南省自然科学三等奖一项。


教学

本科生课程：《电路分析基础》；《电子技术基础》；《高等数学》；《数据通信与计算机网络》等。

研究生课程：《工程伦理》、《神经网络与应用》、《高等电路与系统导论》。

2009年6月获云南大学实验实践教学评比2等奖；

2011年11月指导国家大学生创新性实验计划项目“脉冲耦合神经网络的图像处理及优化计算研究”。

博士生阮小利2018年获云南大学十届研究生科研创新项目；2019年获云南省教育厅科学研究基金项目。

博士生郭延哺2019年获云南大学十一届研究生科研创新项目；2020年获云南省教育厅科学研究基金项目。


代表性SCI论文

  1. A robust infrared and visible image fusion framework via multi-receptive-field attention and color visual perception, Applied Intelligence, 2022, https://doi.org/10.1007/s10489-022-03952-z 

  2. CDMC-Net: Context-Aware Image Deblurring Using a Multi-scale Cascaded Network, Neural Processing Letters, 2022,https://doi.org/10.1007/s11063-022-10976-6 

  3. Deep multi-scale Gaussian residual networks for contextual-aware translation initiation site recognition, Expert Systems With Applications 207 (2022) 118004, https://doi.org/10.1016/j.eswa.2022.118004 

  4. DPNet: Detail-preserving image deraining via learning frequency domain knowledge, Digital Signal Processing 130 (2022) 103740, https://doi.org/10.1016/j.dsp.2022.103740 

  5. Asymmetric Global–Local Mutual Integration Network for RGBT Tracking, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 71, 2022, Digital Object Identifier 10.1109/TIM.2022.3193971. 

  6. A two-stage network with wavelet transformation for single-image deraining，Visual Computer 2022, https://doi.org/10.1007/s00371-022-02533-y 

  7. Context-aware dynamic neural computational models for accurate Poly(A) signal prediction, Neural Networks 152 (2022) 287–299, https://doi.org/10.1016/j.neunet.2022.04.025 

  8. CIRNet: An improved RGBT tracking via cross-modality interaction and re-identification, Neurocomputing 493 (2022) 327–339, https://doi.org/10.1016/j.neucom.2022.04.017 

  9. Rethinking Low-Light Enhancement via Transformer-GAN, IEEE SIGNAL PROCESSING LETTERS, VOL. 29, 2022, DOI 10.1109/LSP.2022.3167331.

  10. An efficient and lightweight image super-resolution with feature supplement network, Optik - International Journal for Light and Electron Optics 255 (2022) 168648, https://doi.org/10.1016/j.ijleo.2022.168648 

  11. Gated residual neural networks with self-normalization for translation initiation site recognition, Knowledge-Based Systems 237 (2022) 107783, https://doi.org/10.1016/j.knosys.2021.107783 

  12. TSE_Fuse: Two stage enhancement method using attention mechanism and feature-linking model for infrared and visible image fusion，Digital SignalProcessing123(2022)103387，https://doi.org/10.1016/j.dsp.2022.103387 

  13. HDINet: Hierarchical Dual-sensor Interaction Network for RGBT Tracking, IEEE SENSORS JOURNAL, 2021, DOI 10.1109/JSEN.2021.3078455.

  14. Siamese networks and multi-scale local extrema scheme for multimodal brain medical image fusion, Biomedical Signal Processing and Control, 68 (2021) 102697, https://doi.org/10.1016/j.bspc.2021.102697. 

  15. A Total Variation with Joint Norms for Infrared and Visible Image Fusion, IEEE Transactions on Multimedia, 2021, DOI 10.1109/TMM.2021.3065496.

  16. UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion, IEEE Transactions on Instrumentation and Measurement, 2021, DOI 10.1109/TIM.2021.3072124.

  17. How to analyze the neurodynamic characteristics of pulse-coupled neural networks? A theoretical analysis and case study of intersecting cortical model, IEEE TRANSACTIONS ON CYBERNETICS, 2021, https://doi.org/10.1109/TCYB.2020.3043233.

  18. Multi-source information exchange encoding with PCNN for medical image fusion, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 31, NO. 3, MARCH 2021, DOI: 10.1109/TCSVT.2020.2998696.

  19. Multimodal medical image fusion based on the spectral total variation and local structural patch measurement, Int J Imaging Syst Technol. 2021;31:391–411. https://doi.org/10.1002/ima.22460. 

  20. Identifying polyadenylation signals with biological embedding via self-attentive gated convolutional highway networks, Applied Soft Computing, 103 (2021) 107133.  https://doi.org/10.1016/j.asoc.2021.107133 .

  21. AMBCR: Low-light image enhancement via attention guided multi-branch construction and retinex theory. IET Image Processing. 2021;1–19. https://doi.org/10.1049/ipr2.12173 .

  22. Attentive gated neural networks for identifying chromatin accessibility, Neural Computing & Applications, 2020, https://doi.org/10.1007/s00521-020-04879-7  .

  23. Construction of high dynamic range image based on gradient information transformation, IET Image Processing, 2020, doi: 10.1049/iet-ipr.2019.0118www.ietdl.org  .

  24. VIF-Net: An Unsupervised Framework for Infrared and Visible Image Fusion, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, VOL. 6, 2020, DOI: 10.1109/TCI.2020.2965304.

  25. Robust spiking cortical model and total-variational decomposition for multimodal medical image fusion, Biomedical Signal Processing and Control, 61 (2020) 101996, https://doi.org/10.1016/j.bspc.2020.101996  .

  26. Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain, BioMed Research International，Volume 2020, Article ID 6265708, 15 pages. https://doi.org/10.1155/2020/6265708  .

  27. DeepANF: A deep attentive neural framework with distributed representation for chromatin accessibility prediction, Neurocomputing, 379 (2020) 305–318, https://doi.org/10.1016/j.neucom.2019.10.091  .

  28. Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix, BioMed Research International, Volume 2020, Article ID 4071508, 13 pages. https://doi.org/10.1155/2020/4071508  .

  29. Multi-focus image fusion combining focus-region-level partition and pulse-coupled neural network, Soft Computing (2019), 23:4685–4699, https://doi.org/10.1007/s00500-018-3118-9

  30.  Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model, Medical & Biological Engineering & Computing (2019) 57:887–900. https://doi.org/10.1007/s11517-018-1935-8.

  31. Analysis of pulse period for passive neuron in pulse coupled neural network, Mathematics and Computers in Simulation 155 (2019) 277–289, https://doi.org/10.1016/j.matcom.2018.05.009.

  32. Infrared and visible image fusion based on convolutional neural network model and saliency detection via hybrid l 0 - l 1 layer decomposition, Journal of Electronic Imaging, 27(6), 063036 (2018), DOI: 10.1117/1.JEI.27.6.063036.

  33. Multi-focus: Focused region finding and multi-scale transform for image fusion,  Neurocomputing,  320 (2018) 157–170, https://doi.org/10.1016/j.neucom.2018.09.018.

  34. A Color Multi-Exposure Image Fusion Approach Using Structural Patch Decomposition, IEEE Access, VOLUME 6, 2018, 42877-42885, DOI 10.1109/ACCESS.2018.2859355.

  35. Fully Convolutional Network-Based Multifocus Image Fusion, Neural Computation 30, 1775–1800 (2018), doi:10.1162/neco_a_01098.

  36. Infrared and visible image fusion combining interesting region detection and nonsubsampled contourlet transform, Journal of Sensors, Volume 2018, Article ID 5754702, 15 pages, https://doi.org/10.1155/2018/5754702 .(2018).

  37. Infrared and v isible images fusion using v isual saliency and optimized spiking cortical model in non-subsampled shearlet transform domain, Multimedia Tools and Applications,  https://doi.org/10.1007/s11042-018-6099-x .(2018).

  38. A lightweight scheme for multi-focus image fusion, Multimedia Tools and Applications (2018), 77:23501–23527, https://doi.org/10.1007/s11042-018-5659-4.

  39. A regularized locality projection-based sparsity discriminant analysis for face recognition, International Journal of Pattern Recognition and Artificial Intelligence (2018), 32(5) 1856006,   https://doi.org/10.1142/S0218001418560062.

  40. Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain, Infrared Physics & Technology (2018) 88: 1–12. https://doi.org/10.1016/j.infrared.2017.10.004.

  41. Multi-focus image fusion method using S-PCNN optimized by particle swarm optimization, Soft Comput (2018), 22:6395–6407 https://doi.org/10.1007/s00500-017-2694-4.

  42. Similarity/dissimilarity calculation methods of DNA sequences: A survey, Journal of Molecular Graphics and Modelling (2017), 76: 342–355. http://dx.doi.org/10.1016/j.jmgm.2017.07.019 .

  43. A survey of infrared and visual image fusion methods, Infrared Physics & Technology (2017) 85: 478–501. http://dx.doi.org/10.1016/j.infrared.2017.07.010.

  44. Similarity/dissimilarity calculation methods of DNA sequences: A survey, Journal of Molecular Graphics and Modelling 76 (2017) 342–355, http://dx.doi.org/10.1016/j.jmgm.2017.07.019.

  45. Global asymptotic stability by complex-valued inequalities for complex-valued neural networks with delays on period time scales, Neurocomputing 219 (2017) 494–501. http://dx.doi.org/10.1016/j.neucom.2016.09.055. 

  46. Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain,J.Appl. Remote Sens. 11(1), 015011 (2017), http://doi.org/10.1117/1.JRS.11.015011.

  47. A novel DNA sequence similarity calculation based on simplified pulse-coupled neural network and Huffman coding, Physica A-STATISTICAL MECHANICS AND ITS APPLICATIONS 461 (2016) 325–338.DOI: 10.1016/j.physa.2016.05.004.

  48. Remote sensing image fusion method in CIELab color space using nonsubsampled shearlet transform and pulse coupled neural networks,Journal of Applied Remote Sensing, 10(2), 025023(2016), doi:10.1117/1.JRS.10.025023.

  49. Multifocus color image fusion based on NSST and PCNN,Journal of Sensors,Vol.2016, 8359602(2016), doi.org/10.1155/2016/8359602.

  50. Facial feature extraction using frequency map series in PCNN,Journal of Sensors, Vol.2016, 5491341(2016), doi.org/10.1155/2016/5491341.

  51. Novel  LMI-based condition on global asymptotic stability for a class of Cohen-Grossberg BAM networks with the extended activation functions, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,Vol.25(6)(2014),1161-1172. DOI: 10.1109/TNNLS.2013.2289855.

  52. New LMI-based conditions for global exponential stability to a class of Cohen-Grossberg BAM networks with delays, Neurocomputing, 121 (2013) 512-522.DOI: 10.1016/j.neucom.2013.05.016.

  53. Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays，Neural Networks, 25 (2012) 94-105. DOI: 10.1016/j.neunet.2011.07.006.

  54. Passivity-based adaptive hybrid synchronization of a new hyperchaotic system with uncertain parameters, The Scientific World Journal, 2012 (2012). DOI: 10.1100/2012/920170.

  55. Periodic solution to Cohen-Grossberg BAM neural networks with delays on time scales, JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 348(10) (2011),2759-2781. DOI: 10.1016/j.jfranklin.2011.08.015.

  56. An analytic model for enhancing IEEE 802.11 point coordination function media access control protocol, European Transactions on Telecommunications, 22(6)(2011) 332-338. DOI: 10.1002/ett.1482.

  57. Existence and global exponential stability of a periodic solution for a discrete-time interval general BAM neural networks，JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 347卷，5期, pp 763-780, 2010. DOI: 10.1016/j.jfranklin.2010.02.007.

  58. Analysis of autowave characteristics for competitive pulse coupled neural network and its application. Neurocomputing, 72 (2009)2331–2336. DOI: 10.1016/j.neucom.2008.12.008.

  59. Global robust exponential stability for second-order Cohen-Grossberg neural networks with multiple delays, NEUROCOMPUTING, 卷: 73,期: 1-3,页: 213-218, 2009,DOI: 10.1016/j.neucom.2009.09.003.

  60. Global asymptotic stability conditions of delayed neural networks, APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION, 卷: 26期: 3页: 372-380, 2005.

  61. On global exponential stability of cellular neural networks with Lipschitz-continuous activation function and variable delays, APPLIED MATHEMATICS AND COMPUTATION, 卷: 151,期: 2,页: 379-392, 2004.DOI: 10.1016/S0096-3003(03)00347-3.

  62. Globally exponential stability conditions for cellular neural networks with time-varying delays, APPLIED MATHEMATICS AND COMPUTATION, 卷: 131,期: 2-3,页: 487-496, 2002,文献号: PII S0096-3003(01)00162-X, DOI: 10.1016/S0096-3003(01)00162-X.

  63. Estimation of attraction domain amd exponential convergence rate of continuous feedback associative memory, APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION, 卷: 22,期: 3,页: 320-325, 2001,DOI: 10.1023/A:1015562605032.

  64. Stability analysis of delayed cellular neural networks, NEURAL NETWORKS, 卷: 11, 期: 9,页: 1601-1605, 1998,DOI: 10.1016/S0893-6080(98)00080-X.


代表性EI期刊及会议论文

1.竞争型脉冲耦合神经网络及用于多约束QoS路由求解, 通信学报, 31卷，1期，pp 65-72, 2010.

2. 基于 Unit-Linking PCNN 和图像熵的图像分割新方法，系统仿真学报，20（1）（2008），222-227.

3. Cognitive radio multi-channel routing algorithm based on a modified PCNN, 2012 International Conference on Advanced Computational Intelligence, 2012/10/18-2012/10/20, pp 549-552, Nanjing, 2012/10/18, 会议论文.

4. QoS routing algorithm using competitive PCNN, Applied Mechanics and Materials, 229-231卷, pp 1908-1912, 2012/7/24.

5. Face detection method using PCNN and skin color model, Advanced Materials Research, 562-564卷, pp 1377-1381, 2012/4/27. 

6. Multi-focus image fusion based on PCNN model, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics(IHMSC 2012), 2012/8/26-2012/8/27, Nanchang, 2012/8/26, 会议论文.

7. Target face detection using pulse coupled neural network and skin color model, The 2th International Conference on Computer Science and Service System, 2012/8/11-2012/8/13, pp 1310-1313, Nanjing, 2012/8/11, 会议论文.

8. An image segmentation method using image enhancement and PCNN with adaptive parameters, Advanced Materials Research, 490-495卷, pp 1251-1255, 2012/5/18.

9. Facial expression recognition algorithm based on PCNN,International Conference of Electrical, Automation and Mechanical Engineering (EAME 2015),会议论文.

10. Block medical image fusion based on adaptive PCNN,978-1-4799-8353-7 /15/$31.00 ©2015 IEEE,会议论文。


代表性中文核心期刊论文

[1]S-PCNN与二维静态小波相结合的遥感图像融合研究，激光与光电子学进展，52,101004(2015)，101004-1-6.

[2]多目标粒子群优化PCNN参数的图像融合算法,中国图象图形学报,2016,21(10):1298-1306. 

[3]基于S-PCNN与DDCT相结合的多传感器图像融合,激光与红外,45(9) 1123-1128,2015.

[4]基于简化脉冲耦合神经网络的噪声人脸识别,云南大学学报（自然科学版），2015,37(5):687-694.

[5]一种基于PCNN的改进型虹膜识别算法,计算机科学,41(11A):110-114,2014.

[6]PCNN 的周期特性分析,云南大学学报( 自然科学版)，2015，37(1):26-30.

[7]基于局部控制核的彩色图像目标检测方法，电子技术应用，2016，42（12）：89-92.

[8]基于简化PCNN与拉普拉斯金字塔分解的彩色图像融合，计算机应用，2016，36（S1）：133-137.

[9]基于拉普拉斯金字塔与 ＰＣＮＮ － ＳＭＬ的图像融合算法，计算机科学,2016,43(6A):122-124.