完整论文发表情况见https://scholar.google.com/citations?user=vM6yGTEAAAAJ&hl=cn

• Yuji Lin, Qian Zhao, Zongsheng Yue, Junhui Hou, and Deyu Meng. Enhancing underwater light field images via global geometry-aware diffusion process. IEEE Transactions on Image Processing, 35: 6886-6899, 2026.

• Yuji Lin, Junhui Hou, Xianqiang Lyu, Qian Zhao, and Deyu Meng. Enhancing underwater imaging with 4-D light fields: Dataset and method. IEEE Journal of Selected Topics in Signal Processing, 19(8): 1617-1631, 2025.

• Shinan Chen, Qian Zhao, Zongsheng Yue, Xiangyong Cao, and Deyu Meng. Learning to generate realistic hyperspectral noise for denoising enhancement. IEEE Transactions on Geoscience and Remote Sensing, 63: A5529217, 2025.

• Yuntao Shou, Xiangyong Cao, Peiqiang Yan, Qiaohui, Qian Zhao, and Deyu Meng. Graph domain adaptation with dual-branch encoder and two-level alignment for whole slide image-based survival prediction. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Honolulu, HI, USA, 2025.

• Zongsheng Yue, Hongwei Yong, Qian Zhao, Lei Zhang, and Deyu Meng. Deep variational network toward blind image restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(11): 7011-7026, 2024.

• Xinyi Liu, Qi Xie, Qian Zhao, Hong Wang, and Deyu Meng. Low-light image enhancement by Retinex-based algorithm unrolling and adjustment. IEEE Transactions on Neural Networks and Learning Systems, 35(11): 15758-15771, 2024.

• Jiangtao Zhang, Zongsheng Yue, Hui Wang, Qian Zhao, and Deyu Meng. Blind image deconvolution by generative-based kernel prior and initializer via latent encoding. In: Proceedings of the 18th European  Conference on Computer Vision (ECCV), Milan, Italy, 2024.

• Zina Li, Yao Wang, Qian Zhao, Shijun Zhang, and Deyu Meng. A tensor-based online RPCA model for compressive background subtraction. IEEE Transactions on Neural Networks and Learning Systems, 34(12): 10668-10682, 2023.

• Qi Xie, Qian Zhao, Zongben Xu, and Deyu Meng. Fourier series expansion based filter parametrization for equivariant convolutions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4): 4537-4551, 2023.

• Jun Shu, Yanwen Zhu, Qian Zhao, Deyu Meng, and Zongben Xu. MLR-SNet: Transferable LR schedules for heterogeneous tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3): 3505-3521, 2023.

• Qian Zhao, Jun Shu, Xiang Yuan, Ziming Liu, and Deyu Meng. A probabilistic formulation for meta-weight-net. IEEE Transactions on Neural Networks and Learning Systems, 34(3): 1194-1208, 2023.

• Qi Xie, Minghao Zhou, Qian Zhao, Zongben Xu, and Deyu Meng. MHF-Net: An interpretable deep network for multispectral and hyperspectral image fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3): 1457-1473, 2022.

• Junxiong Jia, Qian Zhao, Zongben Xu, Deyu Meng, and Yee Leung. Variational Bayes' method for functions with applications to some inverse problems. SIAM Journal on Scientific Computing, 43(1): A355-A383, 2021. 

• Zongsheng Yue, Qian Zhao, Lei Zhang, and Deyu Meng. Dual adversarial network: Toward real noise removal and noise generation. In: Proceedings of the 16th European Conference on Computer Vision (ECCV), Online, 2020.
  

• Qian Zhao, Xiangyu Rui, Zhi Han, and Deyu Meng. Multilinear multitask learning by rank-product regularization. IEEE Transactions on Neural Networks and Learning Systems, 31(4): 1336-1350, 2020.

• Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng. Meta-weight-net: learning an explicit mapping for sample weighting. In: Advances in Neural Information Processing Systems 32 (NeurIPS), Vancouver, Canada, 2019.

• Zongsheng Yue, Hongwei Yong, Qian Zhao, Lei Zhang, and Deyu Meng. Variational denoising network: toward blind noise modeling and removal. In: Advances in Neural Information Processing Systems 32 (NeurIPS), Vancouver, Canada, 2019.

• Jing Yao, Deyu Meng, Qian Zhao, Wenfei Cao, and Zongben Xu. Nonconvex-sparsity and nonlocal-smoothness based blind hyperspectral unmixing. IEEE Transactions on Image Processing, 28(6): 2991-3006, 2019.

• Qi Xie, Qian Zhao, Deyu Meng, and Zongben Xu. Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(8):1888-1902, 2018.

• Deyu Meng, Qian Zhao, andLu Jiang. A theoretical understanding of self-paced learning. Information Sciences, 414:319-328, 2017.

• Xiangyong Cao, Qian Zhao, Deyu Meng, Yang Chen, and Zongben Xu. Robust low-rank matrix factorization under general mixture noise distributions. IEEE Transactions on Image Processing, 25(10):4677-4690, 2016.

• Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu, Shuhang Gu, Wangmeng Zuo, and Lei Zhang. Multispectral images denoising by intrinsic tensor sparsity regularization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016.

• Lingling Wang, Qian Zhao, Jinghuai Gao, Zongben Xu, Michael Fehler, and Xiudi Jiang. Seismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization. Geophysics, 81(2):169-182, 2016.

•  Qian Zhao, Deyu Meng, Xu Kong, Qi Xie, Wenfei Cao, Yao Wang, and Zongben Xu. A novel sparsity measure for tensor recovery. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015.
  

• Qian Zhao, Deyu Meng, Lu Jiang, Qi Xie, Zongben Xu, and Alexander Hauptmann. Self-paced learning for matrix factorization. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), Austin, USA, 2015.

• Lu Jiang, Deyu Meng, Qian Zhao, Shiguang Shan, and Alexander Hauptmann. Self-paced curriculum learning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), Austin, USA, 2015.

• Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, and Yan Yan. L1-norm low-rank matrix factorization by variational Bayesian method. IEEE Transactions on Neural Networks and Learning Systems, 26(4):825-839, 2015.

• Qian Zhao, Deyu Meng, Zongben Xu, Wangmeng Zuo, and Lei Zhang. Robust principal component analysis with complex noise. In: Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, 2014.