基于扩散模型的缺陷检测和归因理论与方法，国家重点研发计划. 执行时间：2024.01.01-2028.12.31（主持）
耦合多重先验信息的低秩张量恢复模型、理论与算法研究. 国家自然科学基金面上项目. 执行时间：2021.01-2024.12.（主持）
基于样本的非线性压缩感知理论及其应用. 国家自然科学基金面上项目. 执行时间：2017.01-2020.12.（主持）
低秩矩阵复原的Schatten-q正则化理论与算法研究. 国家自然科学基金面上项目. 执行时间：2013.01-2016.12（主持）
基于L1/2正则化的压缩传感可重构性理论研究. 国家自然科学基金青年项目. 执行时间：2011.01-2013.12（主持）
关于前馈神经网络结构与本质逼近阶研究. 国家自然科学基金青年项目. 执行时间：2008.01-2011.12（主持）
关于神经网络拓扑选择与逼近阶研究. 教育部科学技术重点项目. 执行时间：2008.01-2010.12（主持）
关于神经网络逼近能力与算法研究. 部委级科研项目面上项目. 执行时间：2008.06-2010.06（主持）
关于前向神经网络逼近复杂性与算法研究. 部委级科研项目一般项目. 执行时间：2009.06-2012.06（主持）
基于Lq极小化的压缩传感理论及应用研究. 中央高校基本科研业务费重点项目，执行时间：2010.10-2013.10（主持）
块稀疏信号重构的非凸极小化方法及算法应用研究. 中央高校基本科研业务费重大项目，执行时间：2015.01-2017.12（主持）
网络上的流行病动力系统的研究. 国家自然科学基金青年项目. 执行时间：2008.01-2010.12（主持子课题一项）
Modified correlated total variation regularization for low-rank matrix recovery
Liu X. L., Dou Y., Wang J.J.
Signal, Image and Video Processing,2024
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Guaranteed matrix recovery using weighted nuclear norm plus weighted total variation minimization
Liu X. L., Peng J. J., Hou J. Y., Wang Y. Wang J.J.
Signal Processing,2024
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Theory and fast learned solver for ℓ^1-TV regularization
Liu X. L., Wang J.J., Jin B. T.
Inverse Problems,2024
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Hybrid plug-and-play CT image restoration using nonconvex low-rank group sparsity and deep denoiser priors
Liu C. Y., Li S., Hu D. L., Zhong Y. X., Wang J.J., Zhang P.
Physics in Medicine & Biology,2024
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Poisson tensor completion with transformed correlated total variation regularization
Feng Q. R., Hou J. Y., Kong W. C., Wang J.J.,
Pattern Recognition,2024
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Exact matrix completion via smooth matrix factorization
Luo X. H., Zhang Z. L., Wang W. D., Wang J.J.
Journal of Electronic Imaging,2024
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Poisson image deblurring with frame-based nonconvex regularization
Feng Q. R., Zhang F., Kong W. C., Wang J.J.
Applied Mathematical Modelling,2024
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Enhanced Low-Rank Tensor Recovery Fusing Reweighted Tensor Correlated Total Variation Regularization for Image Denoising
Huang K., Kong W. C., Zhou M., Qin W. J. Wang J.J.
Journal of Scientific Computing,2024
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Tensor completion via joint reweighted tensor Q-nuclear norm for visual data recovery
Cheng X. Y., Kong W. C., Luo X., Qin W. J., Zhang F., Wang J.J.
Signal Processing,2024
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High-order block RIP for nonconvex block-sparse compressed sensing
Huang J. W., Liu X. L., Hou J. Y., Wang J.J., Zhang F., Jia J. P.
Journal of Inverse and Ill-posed Problems,2024
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Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation
Qin W. J., Wang H. L., Zhang F., Ma W. J., Wang J.J., Huang T. W.
IEEE Transactions on Image Processing,2024
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Nonlocal Tensor Decomposition With Joint Low Rankness and Smoothness for Spectral CT Image Reconstruction
Liu C. Y., Li S., Hu D. L., Wang J.J., Liu C., Zhang P.
IEEE Transactions on Computational Imaging,2024
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Low-tubal-rank tensor completion via local and nonlocal knowledge
Kong W. C., Zhang F., Qin W. J., Feng Q. R., Wang J.J.
Information Sciences,2024
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High-Order Tensor Recovery Coupling Multilayer Subspace Priori with Application in Video Restoration
Tan H., Kong W. C., Zhang F., Qin W. J., Wang J.J.
Proceedings of the 31st ACM International Conference on Multimedia,2023
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Guaranteed tensor recovery fused low-rankness and smoothness
Wang H. L., Peng J. J., Qin W. J., Wang J.J. , Meng D. Y.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2023
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An analysis of noise folding for low-rank matrix recovery
Huang J. W., Zhang F., Wang J.J. , Wang H. L., Liu X. L., Jia J. P.
Analysis and Applications,2023
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Hyperspectral image denoising via nonlocal spectral sparse subspace representation
Wang H. L., Peng J. J., Cao X. Y., Wang J.J. , Zhao Q., Meng D. Y.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2023
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Generalized nonconvex regularization for tensor RPCA and its applications in visual inpainting
Zhang F., Wang H. L., Qin W. J., Zhao X. L., Wang J.J. .
Applied Intelligence,2023
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Fluorescence microscopy images denoising via deep convolutional sparse coding
Chen G., Wang J.J. , Wang H. L., Wen J. M., Gao Y., Xu Y. J.
Signal Processing: Image Communication,2023
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Randomized sampling techniques based low-tubal-rank plus sparse tensor recovery
Zhang F., Yang L. H., Wang J.J. , Luo X.
Knowledge-Based Systems,2023
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The perturbation analysis of nonconvex low-rank matrix robust recovery
Huang J. W., Zhang F, Wang J.J. , Liu X. L., Jia J. P.
IEEE Transactions on Neural Networks and Learning Systems
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One-bit compressed sensing via total variation minimization method
Zhong Y. X., Xu C., Zhang B., Hou J. Y., Wang J.J.
Signal Processing, 2023
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Tensor compressive sensing fused low-rankness and local-smoothness
Liu X. L., Hou J. Y., Peng J. J., Wang H. L., Meng D. Y., Wang J.J.
Proceedings of the AAAI Conference on Artificial Intelligence,2023
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Low-Tubal-Rank tensor recovery with multilayer subspace prior learning
Kong W. C., Zhang F., Qin W. J., Wang J.J.
Pattern Recognition,2023
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Deep plug-and-play for tensor robust principal component analysis
Tan H., Wang J.J. , Kong W. C.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),2023
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Matrix recovery using deep generative priors with low-rank deviations
Yu P. B., Wang J.J. , Xu C.
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),2023
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Robust low-rank matrix recovery fusing local-smoothness
Liu X. L., Hou J. Y., Wang J.J. .
IEEE Signal Processing Letters,2022
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Tensor robust principal component analysis from multi-level quantized observations
Wang J.J., Hou.J., Eldar Y.C.
IEEE Transactions on Information Theory,2022
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Exact decomposition of joint low rankness and local smoothness plus sparse matrices
Peng J., Wang Y., Zhang H., Wang J.J., Meng D.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2022
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low-rank high-order tensor completion with applications in visual data
Qin W., Wang H., Zhang F., Wang J.J. , Luo X., Huang T.
IEEE Transactions on Image Processing,2022
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Robust high-order tensor recovery via nonconvex low-rank approximation
Qin W., Wang H., Ma W., Wang J.J.
Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP),2022
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Robust low-rank tensor reconstruction using high-order t-SVD
Qin W., Wang H., Zhang F., Dai M., Wang J.J.
Journal of Electronic Imaging,2021
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Robust Low-tubal-rank Tensor Recovery from Binary Measurements
Hou J. , Zhang F., Qiu H., Wang J.J., Wang Y., Meng D.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2021
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A novel approach to large-scale dynamically weighted directed network representation
Luo X., Wu H., Wang Z., Wang J.J., Meng D.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2021
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Low-tubal-rank plus Sparse Tensor Recovery with Prior Subspace Information
Zhang F., Wang J.J. , Wang W.D.,Xu C.
IEEE Transactions on Pattern Analysis and Machine Intelligence,2021
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Low-rank matrix recovery via regularized nuclear norm minimization
Wang W., Zhang F., Wang J.J.
Applied and Computational Harmonic Analysis,2021
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Large-scale affine matrix rank minimization with a novel nonconvex regularizer
Wang Z., Liu Y., Luo X., Wang J.J., Gao C., Peng D., Chen W.
IEEE Transactions on Neural Networks and Learning Systems,2021
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Generalized non-convex approach for low-tubal-rank tensor recovery
Wang H., Zhang F., Wang J.J., Huang T., Huang J., Liu X.
IEEE Transactions on Neural Networks and Learning Systems,2021
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Group sparse recovery in impulsive noise via alternating direction method of multipliers
Wang J.J., Huang J.W., Zhang F, Wang W.D.
Applied and Computational Harmonic Analysis,2021
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One-bit tensor completion via transformed tensor singular value decomposition
Hou J., Zhang F., Wang J.J.
Applied Mathematical Modelling,2021
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Estimating structural missing values via low-tubal-rank tensor completion
Wang H., Zhang F., Wang J.J., Wang Y.
Proceedings of the 45th International Conference on Acoustics,2021
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Low-tubal-rank tensor recovery from one-bit measurements
Hou J., Zhang F., Wang Y., Wang J.J.
Proceedings of the 45th International Conference on Acoustics,2021
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Non-convex sparse deviation modeling via generative models
Yang Y., Wang H., Wang J.J.
IEEE International Conference on Acoustics,2021
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CMCS-net: image compressed sensing with convolutional measurement via DCNN
Xie Y., Wang H., Wang J.J.
IET Image Processing,2021
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A denoising convolutional neural network inspired via multi-layer convolutional sparse coding
Wen Z., Wang H., Wang J.J.
Journal of Electronic Imaging,2021
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Uniqueness guarantee of solutions of tensor tubal-rank minimization problem
Zhang F., Hou J., Wang J.J., Wang W.
IEEE Signal Processing Letters,2020
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One-bit Compressed sensing via lp minimization method
Hou J.Y., Wang J.J., Zhang F., Huang J.W.
Inverse Problems,2020
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RIP-based performance guarantee for low-tubal-rank tensor recovery
Zhang F, Wang W.D., Huang J.W., Wang J.J.,Wang Y.
Journal of Computational and Applied Mathematics,2020
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Tensor restricted isometry property analysis for a large class of random measurement ensembles
Zhang F, Wang W.D.,Hou J.Y., Wang J.J., Huang J.W.
Science China .Information Sciences,2021
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A nonconvex penalty function with integral convolution approximation for compressed sensing
Wang J.J., Zhang F., Huang J.W., Wang W.D., Yuan C.
Signal Processing,2019
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Block-sparse signal recovery based on truncated l1- minimisation in non-Gaussian noise
Feng Q, Wang J.J.,Zhang F.
IET Communications,2019
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Image denoising in impulsive noise via weighted Schatten p-norm regularization
Chen G., Wang J.J., Zhang F
Journal of Electronic Imaging,2019
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Sharp sufficient condition of block signal recovery via l2/l1-minimization
Huang J.W., Wang J.J., Wang W.D.
IET Signal Processing,2019
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Enhanced Block-Sparse Signal Recovery Performance via Truncated ℓ2/ℓ1−2 Minimization
Kong W., Wang J.J., Wang W.D., Zhang F.
Journal of Computational Mathematics,2020
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Fast and efficient algorithm for matrix completion via closed-form 2/3-thresholding operator
Wang Z., Wang W., Wang J.J.
Neurocomputing,2019
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On asymptotic of extremes from generalized Maxwell distribution
Huang J.W., Wang J.J.
Bull. Korean Math. Soc,2018
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Block-sparse signal recovery via l2/l1-2minimisation method
Wang, W,D., Wang J.J., Zhang, Z.L.
IET Signal Processing,2018
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Reconstruction Analysis of Block Sparse Signal via Truncated ℓ2/ℓ1-minimization with Redundant Dictionaries
Jia y.L., Wang J.J.,Feng Z.
IET Signal Processing,2018
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New Sufficient Conditions of Signal Recovery with Tight Frames via l1-Analysis Approach
Huang J.W., Wang J.J., Zhang F., Wang, W.D.
IEEE Access,2018
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Higher order expansion for moments of extreme for generalized Maxwell distribution
Huang J.W., Wang J.J.,Luo G.W.,Pu H.
Communications in Statistics - Theory and Methods,2018
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Higher order asymptotic behaviour of partial maxima of random sample from generalized Maxwell distribution under power normalization
Huang J.W., Wang J.J.
Applied Mathematics-A Journal of Chinese Universities,2018
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Sparse signal recovery with prior information by iterative reweighted least squares algorithm
Feng N.C., Wang J.J.,Wang W.D.
Journal of Inverse and Ill-posed Problems,2018
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Perturbations of Compressed Data Separation With Redundant Tight Frames
Zhang F., Wang J.J, Wang,Y., Huang, J., &Wang W.
IEEE Access,2018
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An inertial projection neural network for sparse signal reconstruction via l1− 2 minimization
Zhu L., Wang J.J, He, X., & Zhao Y.
Neurocomputing,2018
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Enhancing Matrix Completion Using a Modified Second-Order Total Variation
Wang W.D., Wang J.J.
Discrete Dynamics in Nature and Society,2018
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A Novel Thresholding Algorithm for Image Deblurring Beyond Nesterov’s Rule
Wang Z., Wang J.J., Wang W.D.
IEEE Access,2018
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Robust Signal Recovery With Highly Coherent Measurement Matrices
Wang W.D., Wang J.J.,Zhang Z.L.
IEEE Signal Processing Letters,2017
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Tail properties and approximate distribution and expansion for extreme of lgmd
Huang J.W., Wang J.J, Luo G.W., He J.
Journal of Inequalities & Applications,2017
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On the rate of convergence of maxima for the generalized Maxwell distribution
Huang J.W., Wang J.J.,Luo G.W.
Statistics: A Journal of Theoretical and Applied Statistics,2017
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Non-convex block-sparse compressed sensing with redundant dictionaries
Liu C.Y., Wang J.J., Wang W.D., Wang, Z.
Iet Signal Processing,2017
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Improved RIP Conditions for Compressed Sensing with Coherent Tight Frames
Wang Y., Wang J.J.
Discrete Dynamics in Nature and Society,2017
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基于非凸极小化的扰动压缩数据分离[J]
刘春燕,王文东,王建军
电子学报,2017
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基于混合l2/l1范数极小化方法的块稀疏信号重构条件[J]
王建军,袁建军,王尧
数学学报,2017
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Nonlinear Compressed Sensing Based on Kernel Sparse Representation
Nie F., Wang J.J., Wang Y., & Jing J.
In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER),2017
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Block-sparse compressed sensing with partially known signal support via non-convex minimisation
He S.Y., Wang Y,Wang J.J Xu Z.B,
Iet Signal Processing,2016
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基于相干性理论的非凸块稀疏压缩感知
王文东, 王建军, 王尧, 张自力
中国科学 信息科学,2016
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Kernel canonical correlation analysis via gradient descent
Cai J, Tang Y,Wang J.J.
Neurocomputing,2016
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A perturbation analysis of block-sparse compressed sensing via mixed l2/l1 minimization
Zhang J.,Wang J.J., Wang W.D.
Neurocomputing,2016
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Perona–Malik Model with a New Diffusion Coefficient for Image Denoising[J]
Yuan J., Wang J.J.
International Journal of Image & Graphics,2016
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Low rank tensor completion via partial sum minimization of singular values
Zhang F,Wang J.J., Jing J.
International Conference on Automatic Control and Information Engineering,2016
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Confirming robustness of fuzzy support vector machine via ξ–α bound
Yang C Y., Wang J.J., Chou J J., et al.
Neurocomputing,2015
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A perturbation analysis of nonconvex block-sparse compressed sensing
Wang J.J., Zhang J., Wang W.D., et al.
Communications in Nonlinear Science & Numerical Simulation,2015
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基于迭代重赋权最小二乘算法的块稀疏压缩感知
王文东, 王尧, 王建军
电子学报,2015
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Restricted p-isometry properties of nonconvex block-sparse compressed sensin
Wang Y., Wang J.J., Xu Z.B.
Signal Processing,2014
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Active contours driven by local intensity and local gradient fitting energies
Yuan J.J., Wang J.J.
International Journal of Pattern Recognition and Artificial Intelligence,2014
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Recovery of Sparse Signal and Nonconvex Minimization
Jing J., Wang J.J.
Applied Mechanics & Materials,2014
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On recovery of block-sparse signals via mixed l2/lq(0
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A note on block-sparse signal recovery with coherent tight frames
Wang Y.,Wang J.J.,Xu Z.B.
Discrete Dynamics in Nature and Society,2013
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Lp Error estimate for minimal norm SBF interpolation
Wang J.J., Yang C. Y., Gu Z.G.
Journal of Inequalities and Applications 2013,2013
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Estimation of Approximation with Jacobi Weights by Multivariate Baskakov Operator
Wang J.J., Guo H.F., Jing J.
Journal of Function Spaces,2013
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Derivatives of multivariate Bernstein operators and smoothness with Jacobi weights
Wang J.J., Peng Z.X.,Duan S.K., Jing J.
Journal of Applied Mathematics,2012
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Estimation of approximating rate for neural networks in L(w,p)
Wang J.J.,Yang C.Y., Jing J.
Journal of Applied Mathematics,2012
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Constructive estimation of approximation for trigonometric neural networks
Wang J.J.,Xu W.H., Zou B.
International Journal of Wavelets, Multiresolution and Information Processing,2012
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Approximation of algebraic and trigonometric polynomials by feedforward neural networks
Wang J.J.,Chen B.L. , Yang C.Y.
Neural Computing & Applications,2012
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L2-Loss Twin Support Vector Machine for Classification
Gao B.B.,Wang J.J., Huang H.
5th International Conference on BioMedical Engineering and Informatics (BMEI),2012
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Estimator for Fuzzy Support Vector Machine
Yang C.Y.,Wang J.J.
Advanced Science Letters,2012
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Neural networks and the best Trigonometric approximation
Wang J.J., Xu Z.B.
Journal of Systems Science and Complexity,2011
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Sparse signal recovery based on lq(0
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Aproximation order for multivariate Durrmeyer operators with Jacobi weights
Wang J.J.,Yang C.Y., Duan S.K.
Abstract & Applied Analysis,2011
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Bernstein 型算子线性组合加Jacobi权逼近及高阶导数的等价定理
彭联勇，王建军
应用数学,2011
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New study of neural networks: the essential order of approximation
Wang J.J., Xu Z.B.
Neural Networks,2011
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Derivatives of Bernstein operators and smoothness with Jacobi weights
Wang J.J., Han G.D., et al.
Taiwanese Journal of Mathematics,2011
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稳健Lq(0
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Approximation with Jacobi weights by Baskakov operators. Taiwanese Journal of Mathematics
Wang J.J., Xu Z.B.
Taiwanese Journal of Mathematics,2009
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Margin calibration in SVM class-imbalanced learning
Yang C.Y., Yang J.S.,Wang J.J.
Neurocomputing,2009
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How to measure the essential approximation capability of a FNN
Wang J.J., Zou B., Chen B.L.
2009 Fifth International Conference on Natural Computation, IEEE Computer Society,2009
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Estimation of covering number in learning theory
Wang J.J., Huang H. Luo Z.T.,Bai l.C.
Fifth International Conference on Semantics, Knowledge and Grid, IEEE Computer Society,2009
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Generalization performance of ERM algorithm with geometrically ergodic markov chain samples
Xu J., Zou B.,Wang J.J.
Fifth International Conference on Natural Computation; IEEE Computer Society,2009
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神经网络的加权本质逼近阶
王建军, 徐宗本
数学年刊:中文版,2009
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多元多项式函数的三层前向神经网络逼近方法
王建军, 徐宗本
计算机学报:中文版,2009
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Baskakov算子线性组合加Jacobi权逼近及高阶导数的正逆定理
王建军, 徐宗本
系统科学与数学,2008
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Constructive approximation method of polynomial by neural networks
Wang J.J., Xu Z.B.,Jing J.
International conference on congnitive neurodynamics(2007), Springer Science Business Media B.V,2008
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Imbalanced SVM learning with margin compensation. Lecture Notes in Computer Science, Germany
Yang C.Y.,Wang J.J., Yang J.S.,Yu G.D.
Springer-Verlag,2008
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Stechkin-marchaud type inequalities with Jacobi weights for Bernstein operators
Wang J.J., Xue Y.C., Li F.J.
Journal of Applied mathematics and computing,2007
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近似指数型神经网络的本质逼近阶
王建军,徐宗本
中国科学,2006
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Multiple positive radial solutions of elliptic equations in an exterior domain. Monatshefte fur mathematik
Han G.D.,Wang J.J.
Monatshefte fur mathematik,2006
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and Meng D.Y., Approximation bound of mixture networks in L(w,p) spaces. Lecture Notes in Computer Science, Germany
Xu Z.B., Wang J.J., and Meng D.Y.
Springer-Verlag 2006,2006
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Baskakov算子加Jacobi权逼近及导数的正逆定理
王建军,薛银川
数学年刊,2006
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Baskakov型算子加权逼近下的Stechkin-Marchand不等式
王建军,薛银川
数学研究与评论,2004
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Approximation bounds by neural networks in L(w, p). Lecture Notes in Computer Science, Germany
Wang J.J., Xu Z.B.,and Xu W.J.
Springer-Verlag,2004
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2022.09.15：基于深度卷积神经网络与压缩感知的图像恢复方法