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Weisheng Dong (董伟生) Professor
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Book chapter:
[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)
Journal papers:
[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.
Conference papers:
[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.