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刘且根

教授/博导

南昌大学信息工程学院副院长

人工智能工业研究院副院长

办公室：信工楼A606 手机号：13732908412

邮箱：liuqiegen@ncu.edu.cn、liuqiegen@hotmail.com

成像与视觉表示实验室：https://www.labxing.com/lab/1018/home

Google学术：https://scholar.google.com/citations?user=T00zMvIAAAAJ&hl=zh-CN

工作实践

  • 12/2020- 至今 ： 南昌大学，教授。

  • 05/2016- 07/2017： 加拿大卡尔加里大学 (University of Calgary)，博士后。

    ​ 导师 Henry Leung 教授，IEEE Fellow。

  • 02/2015- 03/2016：美国伊利诺伊大学香槟分校(UIUC) ，博士后。

    ​ 导师 Zhipei Liang 教授， IEEE Fellow，AIMBE Fellow。

  • 12/2015-12/2020 ：南昌大学，副教授。

  • 10/2013- 02/2015：南昌大学，博士后（校聘副教授）。

  • 04/2012–09/2013：南昌大学，讲师。

教育背景

  • 10/2011–04/2012： 中国科学院深圳先进技术研究院劳伯特医学成像研究中心，医学成像。

  • 09/2008–04/2012：上海交通大学工学博士，生物医学工程。

  • 04/2008–10/2008： 中国科学院－德国马普学会计算生物学伙伴研究所，生物信息学。

  • 09/2006–03/2009：上海交通大学理学硕士，计算数学。

  • 09/2001–07/2005： 赣南师范大学理学学士，应用数学。

获奖情况

  • 获 2019 年江西省双千（首批培养类青年项目） 人才计划

  • 获 2019 年江西省青年井冈人才项目

  • 获 2016 年江西省杰出青年人才计划

部分论文

[1] Q. Liu, H. Leung. Variable augmented neural network for decolorization and multi-exposure fusion,Information Fusion,vol. 46, pp.114-127, 2019.

[2] M. Zhang, M. Li, J. Zhou, Y, Zhu, S. Wang, D. Liang, Y. Chen, Q. Liu. High-dimensional embedding network derived prior for compressive sensing MRI reconstruction, Med. Image Anal., vol. 64, 101717, 2020.

[3] S. Li, B. Qin, Q. Liu, Y. Wang, D. Liang, Multi-channel and multi-model based autoencoding prior for grayscale image restoration, IEEE Trans. Image Process., vol. 29, 142-156, 2020.

[4] Q. Liu, Q. Yang, H. Cheng, S. Wang, M. Zhang, D. Liang, Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors, Magn. Reson. Med., vol. 83, no. 1, pp. 322-336, 2020.

[5] F. Zhang, M. Zhang, B. Qin, Y. Zhang, Z. Xu, D. Liang, Q. Liu, REDAEP: Robust and enhanced denoising autoencodingprior for sparse-view CT reconstruction, IEEE Trans. Radiat. Plasma Med. Sci., 2020.

[6] Q. Liu, K. Yang, J. Luo, Y. Zhu, D. Liang. Highly undersampled magnetic resonance image reconstruction using two-levelBregman method with dictionary updating, IEEE Trans. Med. Imag., 32 (7): 1290-1301, 2013.

[7] Q. Liu, D. Liang, Y. Song, J. Luo, Y. Zhu, W. Li. Augmented Lagrangian based sparse representation method with dictionary updating for image deblurring, SIAM J. Imag. Sci., 6 (3): 1689-1718, 2013.

[8] Q. Liu, S. Wang, L. Ying, X. Peng, Y. Zhu, D. Liang. Adaptive dictionary learning in sparse gradient domain for image recovery, IEEE Trans. Image Process., 22(12): 4652-4663, 2013.

[9] Q. Liu, X. Liu, W. Xie, Y. Wang, D. Liang. GcsDecolor: Gradient correlation similarity for efficient contrast preserving decolorization, IEEE Trans. Image Process., 24(9): 2889-2904, 2015.

[10] Q. Liu, G. Shao, Y. Wang, J. Gao, H. Leung. Log-Euclidean metrics for contrast preserving decolorization, IEEE Trans. Image Process., 26(12): 5772-5783, 2017.

[11] Q. Liu, X. Liu, Y. Wang, H. Leung. Semi-parametric decolorization with Laplacian-based perceptual quality metric, IEEE Trans. Circuits Syst. Video Technol., 27(9): 1856-1868, 2017.

[12] Q. Liu, J. Liu, P. Dong, D. Liang. SGTD: Structure gradient and texture decorrelating regularization for image decomposition, The IEEE International Conference on Computer Vision (ICCV), 1081-1088, 2013.

[13] B. Xiong, Q. Liu, J. Xiong, S. Li, S. Wang, D. Liang. Field-of-Experts filters guided tensor completion, IEEE Trans. Multimedia, vol. 20, no. 9, pp. 2316-2329, 2018.

[14] Y. Liu, Q. Liu, M. Zhang, Q. Yang, S. Wang, D. Liang. IFR-Net: Iterative feature refinement network for compressed sensing MRI, IEEE Trans. Comput. Imag., vol. 6, pp. 434-446, 2020.

[15] W. Zhao, Q. Liu, Y. Lv, B. Qin. Texture variation adaptive image denoising with nonlocal PCA, IEEE Trans. Image Process., vol. 28, no. 11, pp. 5537-5551, 2019.

[16] H. Lu, S. Li, Q. Liu, M. Zhang. MF-LRTC: multi-filters guided low-rank tensor coding for image restoration. Neurocomputing, vol. 303, pp. 88-102, 2018.

[17] J. He, Q. Liu, A.G. Christodoulou, C. Ma, F. Lam, Z.P. Liang. Accelerated high-dimensional MR imaging with sparse sampling using low-rank tensors, IEEE Trans. Med. Imag., 32 (7): 2119-2129, 2016.

[18] J. Xiao, L. Liao, Q. Liu, R. Hu, CISI-Net: Explicit latent content inference and imitated style rendering for image inpainting. AAAI, 354-362, 2019.

[19] S. Wang, Y. Xia, Q. Liu, P. Dong, D. Dagan. Fenchel duality based dictionary learning for restoration of noisy images, IEEE Trans. Image Process., 22(12): 5214-5225, 2013.

[20] S. Wang, S. Tan, Y. Gao, Q. Liu, L. Ying, T. Xiao, Y. Liu, X. Liu, H. Zheng, D. Liang. Learning joint-sparse codes for calibration-free parallel MR imaging (LINDBERG), IEEE Trans. Med. Imag., 37(1): 251-261, 2018.

[21] X. Peng, L. Ying, Q. Liu, Y. Zhu, Y. Liu, X. Qu, X. Liu, H. Zheng, D. Liang. Incorporating reference in parallel imaging and compressed sensing, Magn. Reson. Med., doi: 10.1002/mrm.25272, 2014.

[22] Y. Song, Z. Zhu, Y. Lu, Q. Liu, J. Zhao. Reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning, Magn. Reson. Med., doi: 10.1002/mrm.24734, 2013.

[23] Y. Zhu, Q. Zhang, Q. Liu, Y. Xiang J Wang, X. Liu, H. Zheng, D. Liang, J. Yuan. PANDA-T1rho: Integrating principal component analysis and dictionary learning for fast T1rho mapping, Magn. Reson. Med., doi: 10.1002/mrm.25130, 2014.

[24] Y. Zhou, J. Xu, Q. Liu, C. Li, Z. Liu, M. Wang, H. Zheng, S. Wang, A radiomics approach with CNN for shear-wave elastography breast tumor classification, IEEE Trans. Biomed. Eng., vol. 65, no. 9, pp. 1935-1942, 2018.

[25] B. Qin, M. Jin, D. Hao, Y. Lv, Q. Liu, Y. Zhu, S. Ding, J. Zhao, B. Fei, Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiogram, Pattern Recognition, 87: 38-54, 2019.

[26] S. Wang, J. Lv, Z. He, D. Liang, Y. Chen, M. Zhang, Q. Liu, Denoising auto-encoding priors in undecimated wavelet domain for MR image reconstruction, Neurocomputing, vol. 437, pp. 325-338, 2021.

[27] C Quan, J Zhou, Y Zhu, Y Chen, S Wang, D Liang, Q. Liu, Homotopic Gradients of Generative Density Priors for MR Image Reconstruction. IEEE Trans. Med. Imag, 2021.

[28] Y Zhang, T Lv, R Ge, Q Zhao, D Hu, L Zhang, J Liu, Y Zhang, Q. Liu, CD-Net: Comprehensive Domain Network With Spectral Complementary for DECT Sparse-View Reconstruction, IEEE Trans. Comput. Imag 7, 436-447.

[29] Y. Zhu, Y. Liu, L Ying, Z. Qiu, Q. Liu, Sen Jia, H. Wang, X. Peng, X. Liu, H. Zheng, D. Liang. A 4-minute solution forsubmillimeter whole-brain T1ρ quantification, Magn. Reson. Med, 2021, 85(6): 3299-3307.

[30] H Zhou, Y Wang, Q Liu, Y Wang RNMF-guided deep network for signal separation of GPR without labeled data, IEEE Geosci. Remote. Sens. Lett., 1-5, 2021.

[31] D Hu, Y Zhang, J Liu, C Du, J Zhang, S Luo, G Quan, Q Liu, Y Chen, SPECIAL: Single-shot projection error correction integrated adversarial learning for limited-angle CT, IEEE Trans. Comput. Imag,, 2021.

[32] Y. Zhang, D. Hu, Q. Zhao, G. Quan, J. Liu, Q. Liu, Y. Zhang, G. Coatrieux, Y. Chen, H. Yu, CLEAR: Comprehensive learning enabled adversarial reconstruction for subtle structure enhanced low-dose CT imaging, IEEE Trans. Med. Imag., 2021.