1. 国家自然科学基金青年科学基金项目，61701160，JZ2017GJQN1116，2018.01-2020.12，26.41万元，在研，项目负责人

2. 安徽省自然科学基金青年基金项目，1808085QF186，JZ2018AKZR0066，2018.07-2020.06，10万元，在研，项目负责人

3. 商汤青年科研基金，W2018JSKF0481，2018.11-2019.10，20万元，在研，项目负责人

4. 合肥工业大学学术新人提升计划B项目，JZ2018HGTB0228，2018.05-2019.12，20万元，在研，项目负责人

5. 合肥工业大学学术新人提升计划A项目，JZ2017HGTA0176，2017.03-2018.12，5万元，结题，项目负责人

6. 合肥工业大学校博士专项科研资助基金，JZ2016HGBZ1025，2016.10-2018.09，2万元，结题，项目负责人

一、期刊论文（*表示通讯作者）

1）一作/通讯：

[1]. Yu Liu, Chao Zhang, Juan Cheng, Xun Chen, Z. Jane Wang, “A multi-scale data fusion framework for bone age assessment with convolutional neural networks”, Computers in Biology and Medicine, in press, DOI: https://doi.org/10.1016/j.compbiomed.2019.03.015, 2019.

[2]. Yu Liu, Xun Chen, Rabab Ward, Z. Jane Wang, “Medical image fusion via convolutional sparsity based morphological component analysis”, IEEE Signal Processing Letters, vol. 26, no. 3, pp. 485-489, 2019.

[3] Ming Yin, Xiaoning Liu, Yu Liu*, Xun Chen, “Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain”, IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 1, pp. 49-64, 2019.

[4] Yu Liu, Xun Chen, Zengfu Wang, Z.Jane Wang, Rabab Ward, X. Wang, “Deep Learning for Pixel-level Image Fusion: Recent Advances and Future Prospects,”Information Fusion, vol. 42, pp. 158-173, 2018.  主编邀稿

[5] Yu Liu, Xun Chen, Juan Cheng, Hu Peng, Zengfu Wang, “Infrared and visible image fusion with convolutional neural networks”, International Journal of Wavelets, Multiresolution and Information Processing, vol. 16, no. 3, 1850018: 1-20, 2018.

[6] Yu Liu, Xun Chen, Hu Peng, Zengfu Wang, “Multi-focus image fusion with a deep convolutional neural network”, Information Fusion, vol. 36, pp. 191–207, 2017.  ESI高被引论文

[7] Yu Liu, Baocai Yin, Jun Yu, Zengfu Wang, “Image classification based on convolutional neural networks with cross-level strategy”, Multimedia Tools and Applications, vol. 76, no. 8, pp. 11065-11079, 2017.

[8] Yu Liu, Xun Chen, Rabab Ward, Z.Jane Wang, “Image Fusion With Convolutional Sparse Representation”, IEEE Signal Processing Letters, vol. 23, no. 12, pp. 1882-1886, 2016.

[9] Yu Liu, Shuping Liu, Yang Cao, Zengfu Wang, “Automatic chessboard corner detection method”, IET Image Processing, vol. 10, no. 1, pp. 16-23, 2016.

[10] Yu Liu, Zengfu Wang, “Dense SIFT for ghost-free multi-exposure fusion”, Journal of Visual Communication and Image Representation, vol. 31, pp. 208-224, 2015.

[11] Yu Liu, Shuping Liu, Zengfu Wang, “A general framework for image fusion based on multi-scale transform and sparse representation”, Information Fusion, vol. 24, pp. 147-164, 2015.  ESI热点论文、ESI高被引论文、Most cited article published in INFFUS since 2015

[12] Yu Liu, Zengfu Wang, “Simultaneous image fusion and denoising with adaptive sparse representation”, IET Image Processing, vol. 9, no. 5, pp. 347-357, 2015.  IET Image Processing 2017年度最佳论文，1/230

[13] Yu Liu, Shuping Liu, Zengfu Wang, “Multi-focus image fusion with dense SIFT”, Information Fusion, vol. 23, pp. 139-155, 2015.  ESI高被引论文

[14]刘羽, 汪增福. 结合小波变换和自适应分块的多聚焦图像快速融合, 中国图象图形学报, vol. 18, no. 11, pp. 1435-1444, 2013.

2）合作：

[1] Yu Zhang, Yu Liu, Peng Sun, Han Yan, Xiaolin Zhao, Li Zhang, “IFCNN: A general image fusion framework based on convolutional neural network”, Information Fusion, in press, 2019.

[2] Chang Li, Wei Tao, Juan Cheng, Yu Liu, Xun Chen, “Robust multichannel EEG compressed sensing in the presence of mixed noise”, IEEE Sensors Journal, in press, 2019.

[3] Juan Cheng, Luchang Li, Chang Li, Yu Liu, Aiping Liu, Ruobing Qian, Xun Chen, “Remove diverse artifacts simultaneously from a single-channel EEG based on SSA and ICA: A semi-simulated study”, IEEE Access, vol. 7, pp. 60276-60289, 2019.

[4] Lei Ma, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, Brian Alan Johoson, “Deep learning in remote sensing applications: A meta-analysis and review”, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 152, pp. 166-177, 2019.

[5] Dingyi Li, Yu Liu, Zengfu Wang, “Video super-resolution using non-simultaneous fully recurrent convolutional network”, IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1342-1355, 2019.

[6] Xun Chen, Juan Cheng, Rencheng Song, Yu Liu, Rabab Ward, Z. Jane Wang, “Video-based heart rate measurement: Recent advances and future prospects”, IEEE Transactions on Instrumentation and Measurement, in press, 2018.

[7] Xuesong Wang, Chen Chen, Yuhu Chen, Xun Chen, Yu Liu, “Zero-shot learning based on deep weighted attribute prediction”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, 2018.

[8] Juan Cheng, Fulin Wei, Chang Li, Yu Liu, Aiping Liu, Xun Chen, “Position-independent gesture recognition using sEMG signals via canonical correlation analysis”, Computers in Biology and Medicine, vol. 103, pp. 44-54, 2018.

[9] Chang Li, Yu Liu, Juan Cheng, Rencheng Song, Hu Peng, Qiang Chen, Xun Chen, “Hyperspectral unmixing with bandwise generalized bilinear model”, Remote Sensing, vol. 2018, no. 10, paper ID 1600, pp. 1-10, 2018.

[10] Zhengyuan Xu, Yu Liu, Mingquan Ye, Lei Huang, Hao Yu, Xun Chen, “Patch based collaborative representation with Gabor feature and measurement matrix for face recognition”, Mathematical Problems in Engineering, vol. 2018, paper ID 3025264, pp. 1-13, 2018.

[11] Xun Chen, Aiping Liu, Qiang Chen, Yu Liu, Liang Zou, “Simultaneous Ocular and Muscle Artifact Removal From EEG Data by Exploiting Diverse Statistics”, Computers in Biology and Medicine, vol. 88, pp. 1-10, 2017.

[12] 刘淑萍, 刘羽, 於俊, 汪增福. 结合手指检测和HOG特征的分层静态手势识别, 中国图象图形学报, vol. 20, no. 6, pp. 781-788, 2015. （《中国图象图形学报》2016年度优秀论文）

[13] 殷保才, 刘羽, 汪增福. 结合色度和纹理不变性的运动阴影检测, 中国图象图形学报, vol. 19, no. 6, pp. 896-905, 2014.

二、会议论文（*表示通讯作者）：

[1] Xun Chen, Chao Zhang, Yu Liu*, “Bone Age Assessment with X-Ray Images Based on Contourlet Motivated Deep Convolutional Networks”, 20th IEEE International Workshop on Multimedia Signal Processing (MMSP), Vancouver, Canada, Aug. 29-31, 2018, pp. 1-6.

[2] Dingyi Li, Yu Liu, Zengfu Wang, “Video super-resolution using motion compensation and residual bidirectional recurrent convolutional network”, 24th IEEE International Conference on Image Processing (ICIP), Beijing, China, Sep. 17-20, 2017, pp. 1642-1646.

[3] Yu Liu, Xun Chen, Juan Cheng, Hu Peng, “A medical image fusion method based on convolutional neural networks”, 20th International Conference on Information Fusion (ICIF), Xi’an, China, July 10-13, 2017, pp. 1070-1076.

[4] Yu Liu, Baocai Yin, Jun Yu, Zengfu Wang, “Cross-level: A practical strategy for convolutional neural networks based image classification”, 1st CCF Chinese Conference on Computer Vision (CCCV), Xi’an, China, Sep. 18-20, 2015, CCIS 546, pp. 398-406.

[5]Yu Liu, Shuping Liu, Yang Cao, Zengfu Wang, “A practical algorithm for automatic chessboard corner detection”, 21thIEEE International Conference on Image Processing (ICIP), Paris, France, Oct. 27-30, 2014, pp. 3394-3398.

[6]Yu Liu, Shuping Liu, Zengfu Wang, “Medical Image Fusion by combining nonsubsampled contourlet transform and sparse representation”, 6th Chinese Conference on Pattern Recognition (CCPR), Nov. 17-19, Changsha, China, 2014, pp. 372-381.

[7] Shuping Liu, Yu Liu, Jun Yu, Zengfu Wang, “A static hand gesture recognition algorithm based on Krawtchouk moments”, 6th Chinese Conference on Pattern Recognition (CCPR), Nov. 17-19, Changsha, China, 2014, pp. 321-330.

[8] Yu Liu, Zengfu Wang, “A practical pan-sharpening method with wavelet transform and sparse representation”, 10th IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China, Oct. 22-23, 2013, pp. 288-293.

[9] Yu Liu, Zengfu Wang, “Multi-focus image fusion based on sparse representation with adaptive sparse domain selection”, 7th International Conference on Image and Graphics (ICIG), Qingdao, China, Jul. 26-28, 2013, pp. 591-596.

1. 汪增福, 刘羽，一种实时的多模态医学图像融合方法，中国发明专利，专利授权号：ZL201410427772.3

2. 陈勋，徐雪远，陈强，成娟，刘羽，一种少数通道的脑电信号中肌电伪迹的消除方法，中国发明专利，专利授权号：ZL201710054115.2

3. 刘羽，张超，陈勋，成娟，李畅，宋仁成，基于非下采样轮廓波变换和卷积神经网络的骨龄评估方法，中国发明专利，专利申请号：201810965998.7, 公开号：CN109118487A