Book charpter:

1. Weisheng Dong and Xin Li, Sparsity-Regularized Image Restoration: Locality and Convexity, Image Restoration: Fundamentals and Advances, 115, CRC Press, 2018.
2. Xin Li, Weisheng Dong, Guangming Shi, Sparsity-Based Denoising of Photographic Images: From model-based to data driven, Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends, 2018.
3. Weisheng Dong, Xin Li, and Lei Zhang, “Sparsity-regularized image restoration: locality and convexity revisited,” in Image Restoration: Fundamentals and Advances, CRC Press, Bahadir Gunturk and Xin Li (Editors), 2011. (PDF)

国际期刊：

1. W. Dong, C. Zhou, F. Wu, J. Wu, G. Shi, and X. Li, “Model-guided deep hyperspectral image super-resolution,” IEEE Trans. on Image Processing, in press, 2021.
2. J. Ma, J. Wu, L. Li, W. Dong, X. Xie, G. Shi, and W. Lin, “Blind Image Quality Assessment With Active Inference”, IEEE Trans. on Image Processing, vol. 30, no. 3, pp. 3650-3663, March 2021.
3. Q. Ning, W. Dong, G. Shi, L. Li and X. Li, “Accurate and lightweight image super-resolution with model-guided deep unfolding network,” IEEE Journal of Selected Topics on Signal Processing, vol. 15, no. 2, 240-252, 2021.
4. H. Zhu, L. Li, J. Wu, W. Dong, G. Shi, Generalizable No-Reference Image Quality Assessment via Deep Meta-learning, IEEE Trans. on Circuits and Systems for Video Technology, 2021.
5. F. Wu, T Huang, W. Dong, G. Shi, Z. Zheng, X Li, “Toward blind joint demosaicing and denoising of raw color filter array data”, Neurocomputing, 2021.
6. F. Wu, W. Dong, T. Huang, G. Shi, S. Cheng, X. Li, “Hybrid sparsity learning for image restoration: An iterative and trainable approach”, Signal Processing, vol. 178, 107751, Jan. 2021.
7. T. Huang, W. Dong, J. Liu, F. Wu, G. Shi, and X. Li, “Accelerating convolutional neural network via structured Gaussian scale mixture models: a joint grouping and pruning approach,” IEEE Journal of Selected Topics on Signal Processing, vol. 14, no. 4, pp. 817-827, May, 2020.
8. J. Wu, J. Ma, F. Liang, W. Dong, G. Shi, and W. Lin, “End-to-end blind image quality prediction with cascaded deep neural network”, IEEE Trans. on Image Processing, vol. 29, pp. 7414-7426, 2020.
9. J. Wu, C. Ma, L. Li, W. Dong, and G. Shi, “Probabilistic Undirected Graph Based Denoising Method for Dynamic Vision Sensor”, IEEE Trans. on Multimedia, 2020.
10. J. Wu, W. Yang, L. Li, W. Dong, G. Shi, and W. Lin, “Blind image quality prediction with hierarchical feature aggregation”, Information Sciences, vol. 552, pp. 167-182, 2020.
11. Weisheng Dong, Peiyao Wang, Wotao Yin, Guangming Shi, Fangfang Wu, Xiaotong Lu, Denoising Prior Driven Deep Neural Network for Image Restoration, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , Oct. 2019.
12. W. Dong, H. Wang, F. Wu, G. Shi, and X. Li, “Deep spatial-spectral representation learning for hyperspectral image denoising”, IEEE Trans. on Computational Imaging, in press, 2019.
13. J. Wu, Y. Liu, W. Dong, G. Shi, and W. Lin, “Quality assessment for video with degradation along salient trajectories”, IEEE Trans. on Multimedia, vol. 21, no. 11, pp. 2738-2749, 2019.
14. J. Song, X. Xie, G. Shi, and W. Dong, “Multi-layer discriminative dictionary learning with locality constraint for image classification”, Pattern Recognition, vol. 91, pp. 135-146, 2019.
15. J. Wu, M. Zhang, L. Li, W. Dong, G. Shi, and W. Lin, “No-reference image quality assessment with visual pattern degradation”, Information Sciences, vol. 504, pp. 487-500, 2019.
16. X. He, B. Shi, X. Bai, G. Xia, Z. Zhang, W Dong, Image caption generation with part of speech guidance, Pattern Recognition Letters, vol. 119, pp. 229-237, 2019.
17. J. Wu, J. Zeng, W. Dong, G. Shi, W. Lin, Blind image quality assessment with hierarchy: Degradation from local structure to deep semantics,  Journal of Visual Communication and Image Representation, vol. 58, pp. 353-362, 2019.
18. Y. Zhou, L. Li, J. Wu, K. Gu, W. Dong, and G. Shi, “Blind quality index for multiply distorted images using biorder structure degradation and nonlocal statistics”, IEEE Trans. on Multimedia, vol. 20, no. 11, pp. 3019-3032, 2018.
19. Weisheng Dong, Tao Huang, Guangming Shi, Yi Ma, and Xin Li, “Robust Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image and Video Denoising”, IEEE Journal of Selected Topics in Signal Processing (JSTSP), vol. 12, no. 6, 1435-1448, 2018.
20. Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Jinjian Wu, and Xin Li, “Image super-resolution with parametric sparse model learning”, IEEE Transactions on Image Processing (TIP), vol. 27, no. 9, pp. 4638-4650, 2018.
21. G. Shi, T. Huang, Weisheng Dong, J. Wu, and X. Xie, “Robust Foreground Estimation via Structured Gaussian Scale Mixture Modeling”, IEEE Trans. on Image Processing, vol. 27, no. 10, pp. 4810-4824, 2018.
22. Tao Huang, Weisheng Dong, X. Xie, et al. “Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-rank Approximation,” IEEE Transactions on Image Processing (TIP), vol. 26, no. 7, pp.3171-3186, 2017.
23. Weisheng Dong, Guangming Shi, Xin Li, Jinjian Wu, and Zhenhua Guo, “Color-guided depth recovery via local structural and nonlocal low-rank regularization,” IEEE Transactions on Multimedia, vol. 19, no. 2, pp. 293-301, 2017.
24. Jinjian Wu, Leida Li, Weisheng Dong, Guangming Shi, Weisi Lin, C.-C. Jay Kuo, “Enhanced Just Noticeable Difference Model for Images With Pattern Complexity”, IEEE Trans. on Image Processing, vol. 26, no. 6, pp. 2682-2693, June, 2017.
25. Weisheng Dong, Fazuo Fu, Guangming Shi, and Xun Cao, Jinjian Wu, Guangyu Li, and Xin Li, “Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation”, IEEE Trans. On Image Processing, vol. 25, no. 5, pp. 2337-2352, May 2016. (Paper, Project, Code) (A very effective non-negative dictionary learning and sparse coding algorithm has been proposed!)
26. Weisheng Dong, Guangming Shi, Yi Ma, and Xin Li, “Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture,” International Journal of Computer Vision (IJCV), vol. 114, no. 2, pp. 217-232, Sep. 2015. (Paper) (Denoising Code) (State-of-the-art Image Restoration performance!).
27. Weisheng Dong, Xiaolin Wu, and Guangming Shi, "Sparsity fine tuning in Wavelet domain with application to compressive image reconstruction", IEEE Trans. on Image Processing (TIP), vol. 23, no. 12, pp. 5249-5262, Dec. 2014. (PDF) (Code coming soon)
28. Weisheng Dong, Guangming Shi, Xiaocheng Hu, and Yi Ma, "Nonlocal sparse and low-rank regularization for optical flow estimation," IEEE Trans. on Image Processing (TIP), vol. 23, no. 10, pp. 4527-4538, 2014. (PDF) (Code)
29. Weisheng Dong, Guangming Shi, Xin Li, Yi Ma, and Feng Huang, "Comressive sensing via nonlocal low-rank regularization", IEEE Trans. on Image Processing (TIP), vol. 23, no. 8, pp. 3618-3612, Aug. 2014. (PDF) (Code & Project) (State-of-the-art CS reconstruction performance on both natural images and complex-valued MRI images!)
30. Weisheng Dong, Lei Zhang, Guangming Shi, and Xin Li, “Nonlocally centralized sparse representation for image restoration,” IEEE Trans. on Image Processing (TIP), vol. 22, no. 4, pp. 1620-1630, Apr. 2013. (PDF) (Code) (Excellent image denoising performance!)
31. Weisheng Dong, Lei Zhang, Rastislav Lukac, and Guangming Shi, “Sparse representation based image interpolation with nonlocal autoregressive modeling,” IEEE Trans. on Image Processing (TIP), vol. 22, no. 4, pp. 1382-1394, Apr. 2013. (PDF) (Code)
32. Weisheng Dong, Guangming Shi, and Xin Li, “Nonlocal image restoration with bilateral variance estimation: a low-rank approach,” IEEE Trans. on Image Processing (TIP), vol. 22, no. 2, pp. 700-711, Feb. 2013. (PDF) (Code)
33. Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. on Image Processing (TIP), vol. 20, no. 7, pp. 1838-1857, July 2011. (PDF) (Code)
34. Xiaolin Wu, Weisheng Dong, Xiangjun Zhang, and Guangming Shi, “Model-assisted adaptive recovery of compressed sensing with imaging applications,” IEEE Trans. on Image Processing (TIP), vol. 21, no. 2, Feb. 2012. (PDF)
35. Weisheng Dong, Guangming Shi, Xiaolin Wu, Lei Zhang, “A learning-based method for compressive image recovery,” Journal of Visual Communication and Image Representation, vol. 24, no. 7, pp. 1055-1063, 2013.
36. Weisheng Dong, Xiafang Yang, and Guangming Shi, “Compressive sensing via reweighted TV and nonlocal sparsity regularisation”, Electronic Letters, vol. 49, no. 3, pp. 184-186, 2013.
37. Weisheng Dong, Guangming Shi, Xin Li, Lei Zhang, and Xiaolin Wu, “Image reconstruction with locally adaptive sparsity and nonlocal robust regularization,” Signal Processing: Image Communication, vol. 27, pp. 1109-1122, 2012. (PDF)
38. Lei Zhang, Weisheng Dong, Xiaolin Wu, and Guangming Shi “Spatial-temporal color video reproduction from noisy CFA sequence,” IEEE Trans. On Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 838-847, June 2010. (PDF)
39. Lei Zhang, Weisheng Dong, David Zhang, Guangming Shi, “Two-stage Image Denoising by Principle Component Analysis with Local Pixel Grouping”, Pattern Recognition, vol. 43, pp. 1531-1549, Apr. 2010. (PDF) (Code)
40. Weisheng Dong, Guangming Shi, and Jizheng Xu, “Adaptive nonseparable interpolation for image compression with directional wavelet transform,” IEEE Signal Processing Letters, vol. 15, pp. 233-236, 2008. (PDF)
41. Guangming Shi, Weisheng Dong, Xiaolin Wu, and Lei Zhang, “Context-based adaptive image resolution upconversion,” Journal of Electronic Imaging, vol. 19, 013008, 2010. (PDF) 
42. Weisheng Dong, Guangming Shi, and Li Zhang, “Immune Memory clonal selection algorithms for designing stack filters,” Neurocomputing, pp. 777-784, Jan. 2007.

国际会议：

1. T. Huang, W. Dong, X. Yuan, J. Wu, and G. Shi, “Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging,” IEEE CVPR 2021.
2. X. Lu, H. Huang, W. Dong, G. Shi, and X. Li, “Beyond network pruning: a joint search-and-training approach,” IJCAI, 2020.
3. H. Zhu, L. Li, J. Wu, W. Dong, G. Shi, “MetaIQA: deep meta-learning for no-reference image quality assessment”, CVPR, 2020.
4. Q. Ning, W. Dong, F. Wu, J. Wu, J. Lin, and G. Shi, “Spatial-temporal Gaussian scale mixture modeling for foreground estimation,” AAAI 2020.
5. J. Ma, J. Wu, L. Li, W. Dong, X. Xie, “Active Inference of GAN for No-Reference Image Quality Assessment”, IEEE International Conference on Multimedia and Expo (ICME), 2020.
6. J. Wu, J. Ma, F. Liang, W. Dong, G. Shi, “End-to-End Blind Image Quality Assessment with Cascaded Deep Features”, IEEE International Conference on Multimedia and Expo (ICME), pp. 1858-1863, 2019.
7. F. Wu, Y. Li, J. Han, W. Dong, G Shi, “Perceptual Image Dehazing Based on Generative Adversarial Learning”, Pacific Rim Conference on Multimedia, pp. 877-887, 2018.
8. T. Huang, F. Wu, W. Dong, G. Shi, X Li, “Lightweight deep residue learning for joint color image demosaicking and denoising”, IEEE International Conference on Pattern Recognition (ICPR), pp. 127-132, 2018.
9. W. Wan, J. Wu, G. Shi, Y. Li, W. Dong, “Super-resolution quality assessment: Subjective evaluation database and quality index based on perceptual structure measurement”, IEEE International Conference on Multimedia and Expo (ICME), 2018.
10. Y. Li, W. Dong, X. Xie, G. Shi, X. Li, and D. Xu, "Learning parametric sparse models for image super-resolution," NIPS, 2016.
11. Weisheng Dong, Guangyu Li, Guangming Shi, Xin Li, and Yi Ma, "Low-rank tensor approximation with Laplacian scale mixture modeling for multiframe image denoising", in Proc. IEEE Int. Conf. on Computer Vision (ICCV), 2015. (PDF)
12. Yongbo Li, Weisheng Dong*,  Guangming Shi, and Xuemei Xie, "Learning parametric distributions for image super-resolution: where patch matching meets sparse coding," in Proc. IEEE Int. Conf. on Computer Vision (ICCV), 2015. (PDF)
13. Weisheng Dong, Xin Li, Yi Ma, an Guangming Shi, "Image reconstruction via Bayesian Structured Sparse Coding", IEEE Int. Conf. on Image Processing, 2014. (Oral)
14. Weisheng Dong, Xiaolin Wu, and Guangming Shi, "Sparsity fine tuning in Wavelet domain with application to compressive image reconstruction", IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
15. Weisheng Dong, Guangming Shi, and Xin Li, “Image deblurring with low-rank approximation structured sparse representation,” APSIPA, 2012. (Invited paper) (PDF)
16. Weisheng Dong, Xin Li, Lei Zhang, and Guangming Shi, “Sparsity-based image denoising via dictionary learning and structure clustering,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 457-464, 2011. (PDF), (code) (Oral presentation, acceptance rate: 3.5%=59/1677)
17. Weisheng Dong, Lei Zhang, and Guangming Shi, “Centralized Sparse Representation for Image Restoration,” in Proc. IEEE Int. Conf. on Computer Vision (ICCV), Barcelona, Spain, 2011. (PDF) (Code)
18. Weisheng Dong, Guangming Shi, Lei Zhang, and Xiaolin Wu, “Super-resolution with nonlocal regularized sparse representation,” in Proc. SPIE Visual Communications and Image Processing (VCIP), July 2010. (PDF) (Best Paper Award)
19. Weisheng Dong, Xin Li, Lei Zhang, and Guangming Shi, “Sparsity-based image deblurring with locally adaptive and nonlocally robust regularization,” accept to Proc. IEEE International Conference on Image Processing (ICIP), 2011. (PDF)
20. Weisheng Dong, Xiaolin Wu, Guangming Shi, and Lei Zhang, “Context-based bias removal of statistical models of wavelet coefficients for image denoising,” in Proc. IEEE International Conference on Image Processing (ICIP), Oct. 2009.
21. Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, “Nonlocal back-projection for adaptive image enlargement,” in Proc. IEEE International Conference on Image Processing (ICIP), Oct. 2009. (PDF) (Code)
22. Fangfang Wu, Guangming Shi, Weisheng Dong, and Xiaolin Wu, “Learning-based recovery of compressive sensing with application in multiple description coding,” in Proc. IEEE International Workshop on Multimedia Signal Processing (MMSP), Oct. 2009.
23. Weisheng Dong, Guangming Shi, and Jizheng Xu, “Signal-adapted directional lifting scheme for image compression,” in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1392-1395, 2008.
24. Guangming Shi, Weisheng Dong, and Li Zhang, “A new fast algorithm for training large window stack filters,” in Proc. International Conference on Natural Computation (ICNC), pp. 724-733, 2006. 
25. Guangming Shi, Weisheng Dong, “The design and implementation of stack filter based on immune memory clonal algorithms with hybrid computation,” in Proc. IEEE International Midwest Symposium on Circuits and Systems (IMSCS), pp. 7-10, Aug. 2005.