----2023----

[1]  C. Wu, J. Li, R. Song*, Y. Li, and Q. Du, “RepCPSI: Coordinate-Preserving Proximity Spectral Interaction Network With Reparameterization for Lightweight Spectral Super-Resolution,” IEEE Trans. Geosci. Remote Sens., vol. 61, no. 8, pp. 1–13, 2023, doi: 10.1109/TGRS.2023.3264675. （中科院一区）

[2]  J. Li, Y. Leng, R. Song*, W. Liu, Y. Li, and Q. Du, “MFormer: Taming Masked Transformer for Unsupervised Spectral Reconstruction,” IEEE Trans. Geosci. Remote Sens., vol. 1, pp. 1–1, 2023, doi: 10.1109/TGRS.2023.3264976.（中科院一区）

[3]  C. Wu, J. Li, R. Song*, Y. Li, and Q. Du, “HPRN: Holistic Prior-Embedded Relation Network for Spectral Super-Resolution,” IEEE Trans. Neural Networks Learn. Syst., pp. 1–15, Dec. 2023, doi: 10.1109/TNNLS.2023.3260828.（中科院一区）

[4]  Y. Hai, R. Song*, J. Li, M. Salzmann, and Y. Hu, “Rigidity-Aware Detection for 6D Object Pose Estimation,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023 pp. 8927-8936.（CCF A类）

[5]  Y. Hai, R. Song*, J. Li, and Y. Hu, “Shape-Constraint Recurrent Flow for 6D Object Pose Estimation,”  in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023 pp. 4831-4840.（CCF A类）

[6] Y. Hai, R. Song*, J. Li, and Y. Hu, F, David, "Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation", in ICCV 2023.（CCF A类）

----2022----

[6]  K. Cao, J. Li, R. Song*, and Y. Li, “HE2LM-AD: Hierarchical and efficient attitude determination framework with adaptive error compensation module based on ELM network,” ISPRS J. Photogramm. Remote Sens., vol. 195, no. May 2022, pp. 418–431, 2023, doi: 10.1016/j.isprsjprs.2022.12.010.（中科院一区）

[7]  Y. Li, Y. Zheng, J. Li, R. Song*, and J. Chanussot, “Hyperspectral Pansharpening With Adaptive Feature Modulation-Based Detail Injection Network,” IEEE Trans. Geosci. Remote Sens., vol. 60, 2022, doi: 10.1109/TGRS.2022.3206880.（中科院一区）

[8]  J. Li et al., “Feature guide network with Context Aggregation Pyramid for Remote Sensing Image Segmentation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 1–12, 2022, doi: 10.1109/jstars.2022.3221860.（中科院二区）

[9]  J. Li, Y. Liu, R. Song*, Y. Li, K. Han, and Q. Du, “Sal2RN: A Spatial-Spectral Salient Reinforcement Network for Hyperspectral and LiDAR Data Fusion Classification,” IEEE Trans. Geosci. Remote Sens., vol. 14, no. 8, pp. 1–1, 2022, doi: 10.1109/TGRS.2022.3231930.（中科院一区）

[10] J. Li, Y. Ma, R. Song*, B. Xi, D. Hong, and Q. Du, “A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–13, 2022, doi: 10.1109/TGRS.2022.3213513.（中科院一区）

[11] F. Hao, J. Li, R. Song*, Y. Li, and K. Cao, “Mixed Feature Prediction on Boundary Learning for Point Cloud Semantic Segmentation,” Remote Sens., vol. 14, no. 19, p. 4757, 2022, doi: 10.3390/rs14194757.（中科院二区）

[12] F. Hao, R. Song*, J. Li, K. Cao, and Y. Li, “Cascaded geometric feature modulation network for point cloud processing,” Neurocomputing, vol. 492, pp. 474–487, Jul. 2022, doi: 10.1016/j.neucom.2022.04.007.（中科院二区）

[13] B. Xi et al., “Multi-Direction Networks With Attentional Spectral Prior for Hyperspectral Image Classification,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–15, 2022, doi: 10.1109/TGRS.2020.3047682.（中科院一区）

[14] F. Hao, J. Li, R. Song*, Y. Li, and K. Cao, “Structure-Aware Graph Convolution Network for Point Cloud Parsing,” IEEE Trans. Multimed., pp. 1–13, 2022, doi: 10.1109/TMM.2022.3216951.（中科院一区）

[15] J. Li, S. Du, R. Song*, C. Wu, Y. Li, and Q. Du, “HASIC-Net: Hybrid Attentional Convolutional Neural Network With Structure Information Consistency for Spectral Super-Resolution of RGB Images,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–15, 2022, doi: 10.1109/TGRS.2022.3142258.（中科院一区）

----2021----

[16] J. Li, H. Zhang, R. Song*, W. Xie, Y. Li, and Q. Du, “Structure-Guided Feature Transform Hybrid Residual Network for Remote Sensing Object Detection,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–13, 2022, doi: 10.1109/TGRS.2021.3103964.（中科院一区）

[17] J. Li, S. Zi, R. Song*, Y. Li, Y. Hu, and Q. Du, “A Stepwise Domain Adaptive Segmentation Network With Covariate Shift Alleviation for Remote Sensing Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–15, 2022, doi: 10.1109/TGRS.2022.3152587.（中科院一区）

[18] J. Li et al., “Deep Hybrid 2-D-3-D CNN Based on Dual Second-Order Attention With Camera Spectral Sensitivity Prior for Spectral Super-Resolution,” IEEE Trans. Neural Networks Learn. Syst., vol. PP, pp. 1–12, 2021, doi: 10.1109/TNNLS.2021.3098767.（中科院一区）

[19] B. Han, X. Jia, R. Song*, F. Ran, and P. Rao, “Auto Complementary Exposure Control for High Dynamic Range Video Capturing,” IEEE Access, vol. 9, pp. 144285–144299, 2021, doi: 10.1109/ACCESS.2021.3118416.

[20] J. Li et al., “Hybrid 2-D–3-D Deep Residual Attentional Network With Structure Tensor Constraints for Spectral Super-Resolution of RGB Images,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 3, pp. 2321–2335, Mar. 2021, doi: 10.1109/TGRS.2020.3004934.（中科院一区）

[21] 韩璐瑶, 谭婵, 刘云猛, and 宋锐*, “在轨实时空间目标检测算法研究,” 航天返回与遥感, vol. 42, no. 6, pp. 122–131, 2021, doi: 10.3969/j.issn.1009-8518.2021.06.012.

[22] Y. Xia, Y. Xia, W. Li, R. Song*, K. Cao, and U. Stilla, “ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion,” in Proceedings of the 29th ACM International Conference on Multimedia, Oct. 2021, pp. 1938–1947, doi: 10.1145/3474085.3475348.（CCF A类）

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[23] H. Xia and L. Sun, “Efficient Sample Adaptive Offset Encoding Based on Temporal and Spatial Priori,” Test Eng. Manag., no. 21247, pp. 21247–21255, 2020.

[24] J. Liu, X. Xiong, J. Li, C. Wu, and R. Song*, “Dilated Residual Network Based on Dual Expectation Maximization Attention for Semantic Segmentation of Remote Sensing Images,” in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Sep. 2020, no. 61901343, pp. 1825–1828, doi: 10.1109/IGARSS39084.2020.9324423.

[25] J. Li, C. Wu, R. Song*, Y. Li, and W. Xie, “Residual Augmented Attentional U-Shaped Network for Spectral Reconstruction from RGB Images,” Remote Sens., vol. 13, no. 1, p. 115, Dec. 2020, doi: 10.3390/rs13010115.

[26] J. Li et al., “Hyperspectral Image Super-Resolution by Band Attention Through Adversarial Learning,” IEEE Trans. Geosci. Remote Sens., vol. 58, no. 6, pp. 4304–4318, Jun. 2020, doi: 10.1109/TGRS.2019.2962713.

[27] J. Li, C. Wu, R. Song*, Y. Li, and F. Liu, “Adaptive Weighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images,” in The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 462–465, [Online]. Available: http://arxiv.org/abs/2005.09305.

[28] B. Xi et al., “Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 3683–3700, 2020, doi: 10.1109/JSTARS.2020.3004973.

[29] J. Li, Y. Li, R. Song*, S. Mei, and Q. Du, “Local spectral similarity preserving regularized robust sparse hyperspectral unmixing,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 10, pp. 7756–7769, 2019, doi: 10.1109/TGRS.2019.2916296.

[30] J. Li, R. Cui, B. Li, R. Song*, Y. Li, and Q. Du, “Hyperspectral image super-resolution with 1D-2D attentional convolutional neural network,” Remote Sens., vol. 11, no. 23, pp. 1–21, 2019, doi: 10.3390/rs11232859.

[31] S. Li and R. Song*, “Bilateral adaptive quantization in HEVC,” Multimed. Tools Appl., vol. 78, no. 2, pp. 2385–2399, 2019, doi: 10.1007/s11042-018-6312-y.

[32] R. Song*, Y. Li, Y. Jia, Y. Wang, and P. Rao, “Efficient, robust and divisible paired comparison for subjective quality assessment,” Multimed. Tools Appl., vol. 77, no. 11, pp. 13597–13613, 2018, doi: 10.1007/s11042-017-4977-2.

[33] J. Li, B. Xi, Q. Du, R. Song*, Y. Li, and G. Ren, “Deep kernel extreme-learning machine for the spectral-spatial classification of hyperspectral imagery,” Remote Sensing, vol. 10, no. 12. 2018, doi: 10.3390/rs10122036.

[34] Y. Li, Y. Hu, R. Song*, P. Rao, and Y. Wang, “Coarse-to-Fine PatchMatch for Dense Correspondence,” IEEE Trans. Circuits Syst. Video Technol., vol. 28, no. 9, pp. 2233–2245, 2018, doi: 10.1109/TCSVT.2017.2720175.

[35] R. Song*, Y. Yuan, Y. Li, and Y. Wang, “Extra Sign Bit Hiding Algorithm Based on Recovery of Transform Coefficients,” Circuits, Syst. Signal Process., vol. 37, no. 9, pp. 4128–4135, 2018, doi: 10.1007/s00034-017-0740-1.

[36] Y. Hu, Y. Li, and R. Song*, “Robust Interpolation of Correspondences for Large Displacement Optical Flow,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, no. July, pp. 4791–4799, doi: 10.1109/CVPR.2017.509.

[37] Y. Hu, R. Song*, and Y. Li, “Efficient Coarse-to-Fine Patch Match for Large Displacement Optical Flow,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, pp. 5704–5712, doi: 10.1109/CVPR.2016.615.

[38] Y. Hu, R. Song*, Y. Li, P. Rao, and Y. Wang, “Highly accurate optical flow estimation on superpixel tree,” Image Vis. Comput., vol. 52, pp. 167–177, Aug. 2016, doi: 10.1016/j.imavis.2016.06.004.

[39] Y. Tian, Y. Wang, R. Song*, and H. Song, “Accurate Vehicle detection and counting algorithm for traffic data collection,” in 2015 International Conference on Connected Vehicles and Expo, ICCVE 2015 - Proceedings, 2016, pp. 285–290, [Online]. Available: http://dx.doi.org/10.1109/ICCVE.2015.60.

[40] Y. Jia, Y. Wang, R. Song*, and J. Li, “Decoder side information generation techniques in Wyner-Ziv video coding: a review,” Multimed. Tools Appl., vol. 74, no. 6, pp. 1777–1803, 2015, [Online]. Available: http://dx.doi.org/10.1007/s11042-013-1718-z.

[41]   J. Y. Lin, R. Song*, C.-H. Wu, T. Liu, H. Wang, and C.-C. J. Kuo, “MCL-V: A streaming video quality assessment database,” J. Vis. Commun. Image Represent., vol. 30, pp. 1–9, 2015, [Online]. Available: http://dx.doi.org/10.1016/j.jvcir.2015.02.012.

部分专利

1. 宋锐,李云松,奉玉丽等,降低编码码率的视频编码方法及系统,授权日期：2012/6/6,专利号ZL201010161530.6
2. 宋锐,李云松,仵小波等,高性能宏块编码实现方法,授权日期2012年1月5日,专利号ZL201110002648.9
3. 宋锐,李云松,崔弘飞等,高性能自适应二进制算术编码器,授权日期2012年12月26日,专利号ZL201110057720.8
4. 宋锐 ,李云松,魏维等,支持帧场自适应运动估计的实现方法,授权日期2013年9月25日,专利号：ZL201110266004.0
5. 宋锐,江雄华,贾媛,李云松,王养利,低复杂度高效高动态数字图像的合成方法,授权日期：2017年7月28日,专利：ZL201410598547.6
6. 宋锐,吴火森,贾媛,李云松,王养利,一种基于最小中值梯度滤波的坏点检测及校正方法,授权日期：2017年5月3日,专利号：ZL201410810142.4
7. 宋锐,贾丽敏,贾媛,李云松,王养利,米彦逢,一种基于整数DCT变换的图像噪声估计方法,授权日期：2018.9.14,专利号：ZL201610098531.8
8. 宋锐,胡银林,李云松,贾媛,王养利,祝桂林,一种高效大位移光流估计方法,授权日期：2018.9.3,专利号：ZL201610118042.4
9. 宋锐,米彦逢,贾丽敏,贾媛,李云松,王养利,一种三维滤波去噪算法的去噪处理系统及方法,授权日期：2019.10.16,专利号：ZL201610297995.1
10. 宋锐,祝桂林,胡银林,贾媛,李云松,王养利,基于帧间联系和局部最差的时域融合算法,授权日期：2018.12.6,专利号：ZL201610297994.7
11. 宋锐,袁野清,贾媛,李云松,王养利,赵园伟,一种基于恢复变换系数的改进变换系数符号位隐藏方法,授权日期：2019.3.26,专利号：ZL201610299043.3
12. 宋锐,安亮,贾媛,李云松,王养利,赵园伟,一种基于运动估计和时空域相关性的快速帧间预测方法,授权日期：2019.1.10,专利号：ZL201610300137.8
13. 宋锐,孙力,贾媛,李云松,王养利,赵园伟,一种 HEVC 基于时间相关性和帧内预测方向的 SAO 优化方法,授权日期：2019.11.5,专利号：ZL201610298041.2
14. 宋锐,李星霓,田野,贾媛,李云松,王养利,许全优,一种基于轮廓有效性提高三维重建点云稠密程度的方法,授权日期：2018.10.26,专利号：ZL201610298507.9
15. 宋锐,李璐,李云松,王养利,赵园伟,一种基于Hadamard变换的帧内预测的快速模式选择和PU划分的方法,授权日期：2019.1.10,专利号：ZL201610348075.8
16. 宋锐,李三春,李云松,贾媛,王养利,赵园伟,一种系数级自适应量化方法,授权日期：2019.1.14,专利号：ZL201610349026.6
17. 宋锐,胡银林,李云松,王养利,一种基于金字塔逐层传播聚类的超像素分割方法,申请日期：2016年5月25日,申请号：201610348500.3
18. 宋锐,胡银林,李云松,王养利,一种基于两级边缘敏感滤波的高精确光流估计方法,申请日期：2016/11/16,申请号：201610423386.6
19. 宋锐,田野,李星霓,贾媛,李云松,王养利,许全优,一种基于邻域块匹配提高三维重建点云稠密程度的方法,授权日期：2019.8.14,专利号：ZL201611201364.1
20. 宋锐,杨星辉,李云松,贾媛,王养利,一种基于局部参考点的快速点云配准方法,申请日期：2018/3/22,申请号：201810241936.1