聂仁灿

办公地点：信息学院1111室

学术兼职

云南省高等学校电工电子基础课程教学指导委员会秘书长、云南省互联网协会六届理事、云南省信息通信工程高级工程师评审委员会委员、中国高校电工电子在线开放课程联盟西南地区工作委员会委员。

研究领域

人工神经网络、多媒体通信与智能信息处理。

研究概况

主持国家自然科学基金、云南省应用基础研究计划重点项目、省重大科技专项计划等项目近10项，在IEEE-TMM、TCSVT等国内外高水平学术期刊发表论文100余篇，获云南省自然科学三等奖1项。入选云南省高层次人才培养支持计划“青年拔尖人才”专项。

研究课题

[1]国家基金地区项目：“3D多模态医学影像融合关键技术研究” (62562062), 2026.1.1-2029.12.31，主持。

[2]国家基金地区项目：“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” (61966037), 2020.1-2023.12，主持。

[3]国家自然科学基金项目“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） 2015.1-2018.12，主持。

[4]云南省基础研究计划重点项目：“高级视觉任务驱动的红外与可见光图像融合研究”（202301AS070025），2023.6-至今，主持。

[5]云南省重大科技专项计划：“基于人工智能技术的主动脉夹层自动分割、诊断与疾病预测模型的研发”，主持(子课题)（202402AA310054），2024.10-至今。

[6]云南省重大科技专项计划：“货柜车件箱自动装卸车码垛机器人关键技术研究与应用”，2025.08-至今，主持(子课题)。

[7]中国博士后科学基金面上项目：“多源图像融合的脉冲信息交换表示与贡献度量方法研究”(2017M621586)，2018.1-2020.12，主持。

奖励与荣誉

(1)云南省科学技术奖，三等，人工神经网络动力学机制研究及应用, 2020。

(2)云南省高层次人才培养支持计划：青年拔尖人才专项。

(3)国家级一流本科课程：数字电路与逻辑设计实验（排名第二）。

学术成果

[1]R. Nie, C. Ma, J. Cao*, D. Zhou, H. Ding. A Total Variation with Joint Norms for Infrared and Visible Image Fusion[J], IEEE Transactions on Multimedia, 24:1460-1472, 2021. 

[2]R. Nie, J. Cao, D*. Zhou, W. Qian. Multi-Source Information Exchange Encoding with PCNN for Medical Image Fusion[J], IEEE Transactions on Circuits and Systems for Video Technology, 31(3): 986-1000, 2020. 

[3]G. Xie, R. Nie, J. Cao, H. Li and J. Li. A Deep Multiresolution Representation Framework for Pansharpening[J], IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2024.3394533, 2024. 

[4]B. Jin, R. Nie*, J. Cao, Y. Zhang, and D. Li. CHFusion: A Cross-modality High-resolution Representation Framework for Infrared and Visible Image Fusion[J], IEEE Transactions on Multimedia, doi: 10.1109/TMM.2023.3294814, 2023. 

[5]X. Guo, R. Nie*, J. Cao, D. Zhou, L. Mei, K. He. FuseGAN: Learning to Fuse Multi-Focus Image via Conditional Generative Adversarial Network[J], IEEE Transactions on Multimedia, 21(8): 1982-1996, 2019. 

[6]Y. Zhang, R. Nie*, J. Cao, C. Ma. Self-Supervised Fusion for Multi-Modal Medical Images via Contrastive Auto-Encoding and Convolutional Information Exchange[J], IEEE Computational Intelligence Magazine, 18(1): 68-80, 2023. 

[7]C. Wang, R. Nie*, J. Cao, X. Wang, Y. Zhang. IGNFusion: An Unsupervised Information Gate Network for Multimodal Medical Image Fusion[J], IEEE Journal of Selected Topics in Signal Processing, 16(4): 854-868, 2022. 

[8]G. Zhang, R. Nie*, and J. Cao. SSL-WAEIE: Self-Supervised Learning with Weighted Auto-Encoding and Information Exchange for Infrared and Visible Image Fusion[J], IEEE-CAA Journal of Automatica Sinica, 9(9): 1694-1697, 2022. 

[9]M. Tan, R. Nie*, J. Cao, Y. Zhang. SMDFusion: A Self-Supervised Fusion for Infrared and Visible Images via Cross-modal Random Noise Masked Encoding and Difference Perception[J]. IEEE Transactions on Consumer Electronics, 71(2): 2579-2591, 2025.

[10]C. Ma, R. Nie*, H. Ding, J. Cao, J. Mei. A Fractional-Order Variation with a Novel Norm to Fuse Infrared and Visible Images[J], IEEE Transactions on Instrumentation and Measurement, 72: 1-12, 2023. 

[11]H. Li, R. Nie*, J. Cao, B. Jin and Y. Han. MPEFNet: Multilevel Progressive Enhancement Fusion Network for Pansharpening[J], IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16:9358-9368, 2023. 

[12]H. Li, R. Nie*, J. Cao, X. Guo, D. Zhou, K. He. Multi-focus image fusion using u-shaped networks with a hybrid objective[J], IEEE Sensors Journal, 19(21): 9755-9765, 2019. 

[13]S. Liang, R. Nie*, J. Cao, X. Wang, G. Zhang. FCF: Feature complement fusion network for detecting COVID-19 through CT scan images[J], Applied Soft Computing, 109111, 2022. 

[14]Y. Zhang, R. Nie*, J. Cao, C. Ma, and C. Wang. SS-SSAN: a self-supervised subspace attentional network for multi-modal medical image fusion[J], Artificial Intelligence Review, 56(S1):421-443, 2023. 

[15]J. Li, R. Nie*, J. Cao, G. Xie and Z. Ding. LRFE-CL: A self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning[J], Expert Systems with Applications, 251: 124125, 2024. 

[16]H. Xu, R. Nie*, J. Cao, G. Xie and Z. Ding. IMQFusion: Infrared and visible image fusion via implicit multi-resolution preservation and query aggregation[J], Expert Systems with Applications, 257: 125014, 2024. 

[17]X. Li, R. Nie*, J. Cao, K. Qing, J. Zuo, and W. Shi. HRSGD: High-order recurrent medical image fusion via self-supervised generative distillation[J]. Neurocomputing, 647:130545, 2025.

[18]L. Shi, R. Nie*, J. Cao, X. Liu. SCMFusion: Semantic Constrained Multi-Scale Fusion Network for infrared and visible image fusion[J]. Optics & Laser Technology, 190, 113097, 2025.

[19]J. Zuo, R. Nie*, J. Cao, W. Shi, X. Li. SARLFuse: A Scale-Aware Representation Learning to fuse infrared and visible images[J]. Displays, 647:103118, 2025.

[20]L. Pan, R. Nie*, G. Zhang, J. Cao, and Y. Han. WAE-TLDN: self-supervised fusion for multimodal medical images via a weighted autoencoder and a tensor low-rank decomposition network[J]. Applied Intelligence, 2024, doi:10.1007/s10489-023-05097-z. 

[21]J. Huang, R. Nie*, J. Cao, Y. Zhang, and H. Su. HP-CRL: High-resolution preservation driven collaborative representation learning for infrared and visible image fusion[J], Optics & Laser Technology, 2024, 177: 111184. 

[22]Y. Han, R. Nie*, J. Cao, S. Liang, and L. Pan. IE-CFRN: Information exchange-based collaborative feature representation network for multi-modal medical image fusion[J], Biomedical Signal Processing and Control, 86:105301, 2023. 

[23]M. He, S. Yu, R. Nie*, C. Wang, X. Wang. Preference Learning to Multi-Focus Image Fusion via Generative Adversarial Network[J], IEEE Transactions on Cognitive and Developmental Systems, 14(4): 1604-1614, 2021. 

[24]G. Zhang, R. Nie*, J. Cao, L. Chen, Y. Zhu. FDGNet: A pair feature difference guided network for multimodal medical image fusion[J], Biomedical Signal Processing and Control, 81: 104545, 2022. 

[25]X. Yang, R. Nie*, G. Zhang, L. Chen, H. Li. DPAFNet: A Multistage Dense-Parallel Attention Fusion Network for Pansharpening[J], Remote Sensing, 14(21): 5539, 2022. 

[26]Z. Guan, X. Wang, R. Nie*, S. Yu, C. Wang. NCDCN: multi-focus image fusion via nest connection and dilated convolution network[J], Applied Intelligence, 1-16, 2022. 

[27]L. Chen, X. Wang, Y. Zhu, R. Nie*. Multi-level difference information replenishment for medical image fusion[J], Applied Intelligence, 53(4): 4579-4591, 2022. 

[28]H. Xu*, R. Nie, J. Cao, M. Tan, and Z. Ding. MADMFuse: A multi-attribute diffusion model to fuse infrared and visible images[J], Digital Signal Processing, 2024, 155: 104741. 

[29]Z. Yan, R. Nie*, J. Cao, G. Xie and Z. Ding. MSL-CCRN: Multi-stage self-supervised learning based cross-modality contrastive representation network for infrared and visible image fusion[J], Digital Signal Processing, 2025, 156: 104853. 

[30]H. Su R. Nie*, J. Cao, Y. Zhang, and J. Huang. A self-supervised fusion for infrared and visible images via multi-level contrastive auto-encoding[J], Infrared Physics & Technology, 2024, 140: 105421. 

[31]R. Hou, D. Zhou, R. Nie, D. Liu, L. Xiong, Y. Guo, C. Yu. VIF-Net: an unsupervised framework for infrared and visible image fusion[J], IEEE Transactions on Computational Imaging, 6, 640-651, 2020. 

[32]Y. Zang, D. Zhou, C. Wang, R. Nie, Y. Guo. UFA-FUSE: A novel deep supervised and hybrid model for multifocus image fusion[J], IEEE Transactions on Instrumentation and Measurement, 70, 1-17, 2021. 

[33]C. Wang, Y. Pu, X. Wang, C. Ma, and R. Nie*. MCNN: Conditional focus probability learning to multi-focus image fusion via mutually coupled neural network[J], IET Image Processing, 17(8): 2422-2436, 2023. 

[34]Y. Zhu, X. Wang, L. Chen, R. Nie*. CEFusion: Multi-Modal medical image fusion via cross encoder[J], IET Image Processing, 16(12): 3177-3189, 2022.