Publication

See also my publication list in Google Scholar

Upcoming
  • Self-supervised blind image deconvolution via deep generative ensemble learning
    M. Chen, Y. Quan, Y. Xu and H. Ji
    IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), xxx(x), xxx, 2022
2023
  • Unsupervised deep video denoising with untrained network
    H. Zheng, T. Pang, and H. Ji
    37th AAAI Conference on Artificial Intelligence (AAAI), Washington, Feb, 2023
  • Unsupervised deep learning for phase retrieval via teacher-student distillation
    Y. Quan, Z. Chen, T. Pang, and H. Ji
    37th AAAI Conference on Artificial Intelligence (AAAI), Washington, Feb, 2023
2022
  • Self-supervised low-light image enhancement using discrepant untrained network priors [PDF] [Github]
    J. Liang, Y. Xu, Y. Quan, B. Shi, and H. Ji,
    IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 32(11), 7332–7345 Nov., 2022
  • Non-blind image deblurring via deep learning in complex field [PDF]
    Y. Quan, P. Lin, Y. Xu, Y. Nan, and H. Ji,
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 33(10), 5387-5400, Oct. 2022
  • Dual-domain self-supervised learning and model adaption for deep compressive imaging [PDF]
    Y. Quan, X. Qin, T. Pang, and H. Ji
    European Conference on Computer Vision (ECCV), Tel Aviv,, Oct., 2022
  • Learning deep non-blind image deconvolution without ground truths [PDF]
    Y. Quan, Z. Chen, H. Zheng, and H. Ji
    European Conference on Computer Vision (ECCV), Tel Aviv,, Oct., 2022
  • A dataset-free deep learning method for Low-Dose CT image reconstruction [PDF]
    Q. Ding, H. Ji, Y. Quan and X. Zhang
    Inverse Problems, (IP), 38, 104003, Sep. 2022
  • L1-norm regularisation for short-and-sparse blind deconvolution: Point source separability and region selection [PDF]
    W. Wang, J. Li, and H. Ji
    SIAM Journal on Imaging Sciences, (SIAM SIIMS), 15(3), 1345-1372, 2022
  • Unsupervised deep background matting using deep matte prior [PDF] [Github]
    Y. Xu, B. Liu, Y. Quan, and H. Ji,
    IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 32(7), 4324-4337, Jul., 2022
  • Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network [PDF] [Github]
    M. Chen, Y. Quan, T. Pang, and H. Ji
    International Journal of Computer Vision, (IJCV),130, 1770–1789, Jul., 2022
  • Self-supervised deep image restoration via adaptive stochastic gradient Langevin dynamics [PDF]
    W. Wang, J. Li and H. Ji
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, Jun., 2022
  • Unsupervised phase retrieval using deep approximate MMSE estimation [PDF]
    M. Chen, P. Lin, Y. Quan, T. Pang, and H. Ji
    IEEE Transactions on Signal Processing, (IEEE TSP), 70, 2239-2252, May, 2022
  • Unsupervised learning for blind image deconvolution via Monte-Carlo sampling [PDF]
    J. Li, Y. Nan, and H. Ji
    Inverse Problem, (IP), 38(3), 035012, Feb. 2022
2021
  • Gaussian kernel mixture network for single image defocus deblurring [PDF] [Github]
    Y. Quan, Z. Wu, H. Ji,
    Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS), Dec., 2021
  • Learnable multi-scale Fourier interpolation for sparse view CT image reconstruction [PDF]
    Q. Ding, H. Ji, H. Gao and X. Zhang,
    24th International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), Oct., 2021
  • Deep learning with adaptive hyper-parameters for low-dose CT image reconstruction [PDF]
    Q. Ding, Y. Nan, H. Gao and H. Ji,
    IEEE Transactions on Computational Imaging (TCI), 7, 648-660, Jun., 2021
  • Recorrupted-to-Recorrupted: Unsupervised deep learning for image denoising [PDF] [Github]
    T. Pang, H. Zheng, Y. Quan, and H. Ji,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, Jun., 2021
  • Texture recognition via exploiting cross-layer statistical self-similarity [PDF]
    Y. Quan, Z. Chen, F. Li, Y. Xu, and H. Ji,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR
    ), Nashville, Jun., 2021
  • Watermarking deep neural networks in image processing [PDF] [Github]
    Y. Quan, H. Teng, Y. Chen, and H. Ji,
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 32(5), 1852-1865, May, 2021
  • Attentive deep network for blind motion deblurring on dynamic scenes [PDF]
    Y. Xu, Y. Zhu, Y. Quan, and H. Ji,
    Computer Vision and Image Understanding (CVIU), 205, 103169, Apr., 2021
  • Image denoising using complex-valued deep CNN [PDF] [Github]
    Y. Quan, Y. Chen, Y. Shao, H. Teng, Y. Xu, and H. Ji,
    Pattern recognition (PR), 111, Mar. 2021
  • Rethinking medical image reconstruction via shape prior, going deeper and faster: Deep joint indirect registration and reconstruction [Link]
    J. Liu, A. Aviles-Rivero, H. Ji, and C. Schonlieb,
    Medical Image Analysis (MedIA), 68, 101930, Feb., 2021
2020
  • Self-supervised Bayesian deep learning for image recovery with applications to compressed sensing [PDF] [Github]
    T. Pang, Y. Quan, and H. Ji,
    European Conference on Computer Vision (ECCV), Aug., 2020
  • Multi-scale discrete framelet transform for graph-structured signals  [PDF]
    H. Ji, Z. Shen, and Y. Zhao, 
    SIAM Journal on Multiscale Modeling and Simulation (SIAM MMS), 18(3), 1210–1241, Jul., 2020
  • Cartoon-texture image decomposition using orientation characteristics in patch recurrence [PDF]
    R. Xu, Y. Xu, Y. Quan, and H. Ji,
    SIAM Journal on Imaging Sciences (SIAM SIIMS), 13(3), 1179–1210, 2020
  • Learnable Douglas-Rachford iteration and its applications in DOT imaging [PDF] [Github]
    J. Liu, N. Chen, and H. Ji,
    Inverse Problems and Imaging (IPI), 14(4), Aug., 2020
  • Low-dose CT with deep learning regularization via proximal forward backward splitting [Link]
    Q Ding, G Chen, X Zhang, Q Huang, H Ji, H Gao,
    Physics in Medicine & Biology, 65(12), 125009, Jun., 2020
  • Self2Self with dropout: Learning self-supervised denoising from single image [PDF] [Github]
    Y. Quan, M. Chen, T. Pang, and H. Ji,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Jun. 2020
  • Variational-EM-based deep learning for noise-blind image deblurring [PDF], [GitHub]
    Y. Nan, Y. Quan, and H. Ji,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Jun. 2020
  • Deep Learning for handling kernel/model uncertainty in image deconvolution [PDF] [GitHub]
    Y. Nan and H. Ji,
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Jun. 2020
  • Collaborative deep learning for super-resolving blurry text images [PDF] [Github]
    Y. Quan, J. Yang, Y. Chen, Y. Xu, and H. Ji,
    IEEE Transactions on Computational Imaging (IEEE TCI), 6, 778–790, Mar., 2020
  • Image denoising via sequential ensemble learning [PDF] [Github]
    X. Yang, Y. Xu, Y. Quan, and H. Ji,
    IEEE Transactions on Image Processing (IEEE TIP), 29, 5038-5049, Mar., 2020
  • Removing reflection from a single image with ghosting effect [PDF]
    Y. Huang, Y. Quan, Y. Xu, R. Xu, and H. Ji,
    IEEE Transactions on Computational Imaging (IEEE TCI), 6, 34-45, Feb. 2020
2019
  • Barzilai-Borwein-based adaptive learning rate for deep learning [PDF]
    J. Liang, Y. Xu, C. Bao, Y. Quan and H. Ji,
    Pattern Recognition Letter, 128(1), 197-203, Dec. 2019
  • Attention with structure regularization for action recognition [PDF]
    Y. Quan, Y. Chen and R. Xu, and H. Ji,
    Computer Vision and Image Understanding (CVIU), 187, 102704, Oct. 2019
  • Deep learning for seeing through window with raindrops [PDF]  [Github]
    Y. Quan, S. Deng, Y. Chen, and H. Ji,
    International Conference on Computer Vision (ICCV), Seoul, 2019
  • Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations [Link]
    C. Wee, C. Liu, A. Lee, J. Poh, H. Ji, and A. Qiu,
    NeuroImage: Clinical, 23, 101929, 2019
  • Digital Gabor filters do generate MRA-based wavelet tight frames  [PDF]
    H. Ji, Z. Shen, and Y. Zhao, 
    Applied and Computational Harmonic Analysis  (ACHA), 47(1), 87-108, Jul. 2019
  • A variational EM framework with adaptive edge selection for blind motion deblurring [PDF]
    L. Yang and H. Ji,
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Los Angeles, 2019
2018
  • Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1  [Link]
    G. Zhu, W. Liu, C. Bao, D. Tong, H. Ji, Z. Shen, D. Yang, L. Lu, 
    Proteins: Structure, Function, and Bioinformatics, 86(5), 501-514, 2018
  • Coherence retrieval using trace regularization  [PDF]
    C. Bao, G. Barbastathis, H. Ji, Z. Shen, and Z. Zhang, 
    SIAM Journal on Imaging Sciences (SIAM SIIMS), 11(1), 679–706, Mar. 2018
  • Digital Gabor filters with MRA structure  [PDF]
    H. Ji, Z. Shen, and Y. Zhao, 
    SIAM Journal on Multiscale Modeling and Simulation (SIAM MMS), 16(1), 452–476. Mar. 2018
2017
  • Apparent coherence loss in phase space tomography  [PDF]
    C. Bao, G. Barbastathis, H. Ji, Z. Shen, and Z. Zhang, 
    Journal of the Optical Society of America A34(11), (JOSA), 2025-2033, 2017
  • Estimating defocus blur through rank of local patches  [PDF][Code]
    G. Xu, Y. Quan and H. Ji,
    International Conference on Computer Vision (ICCV), Venice, 2017
  • Directional frames for image recovery: Multi-scale discrete Gabor frames  [PDF]
    H. Ji, Z. Shen and Y. Zhao, 
    Journal of Fourier Analysis and Applications (JFAA), 23(4), 729-757, Aug. 2017
2016
  • Image recovery via geometrically structured approximation  [PDF]
    H. Ji, Y. Luo and Z. Shen, 
    Applied and Computational Harmonic Analysis, (ACHA), 41(1), 75-93, Jul. 2016
  • An augmented Lagrangian method for L1-regularized optimization problems with orthogonality constraints  [PDF]
    W. Chen, H. Ji and Y. You, 
    SIAM Journal on Scientific Computing (SIAM SISC), 38(4), B570-B592, 2016
  • Dictionary learning for sparse coding: Algorithms and analysis  [PDF]
    C. Bao, H. Ji, Y. Quan and Z. Shen, 
    IEEE Transactions on Pattern Analysis and Machine Intelligence, (IEEE PAMI), 38(7), 1356-1369, Jul. 2016
  • Equiangular kernel Dictionary learning with applications to dynamic texture analysis  [PDF]
    Y. Quan, C. Bao and H. Ji, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
  • Sparse coding for classification via discrimination ensemble  [PDF]
    Y. Quan, Y. Xu, Y. Sun, Y. Huang and H. Ji, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
  • Cerebellar functional parcellation using sparse dictionary learning clustering  [Link]
    C. Wang, J. Kipping, C. Bao, H. Ji and A. Qiu, 
    Frontiers in Neuroscience, 10(188), May. 2016
  • Dual Gramian analysis: Duality principle and unitary extension principle  [PDF]
    Z. Fan, H. Ji and Z. Shen, 
    AMS Mathematics of Computation (AMS MCOM), 85, 239-270, 2016
2015
  • Dynamic texture recognition via orthogonal tensor dictionary learning  [PDF]
    Y. Quan, Y. Huang and H. Ji, 
    International Conference on Computer Vision (ICCV), 2015
  • Removing rain from a single image via discriminative sparse coding  [PDF],  [Code]
    Y. Luo, Y. Xu and H. Ji, 
    International Conference on Computer Vision (ICCV), 2015
  • Classifying dynamic textures via spatiotemporal fractal analysis  [PDF]
    Y. Xu, Y. Quan, Z. Zhang, H. Ling and H. Ji,  
    Pattern Recognition48(10) (PR), 3239-3248, Oct. 2015
  • Data-driven multi-scale non-local wavelet frame construction and image recovery  [PDF], [Code]
    Y. Quan, H. Ji and Z. Shen, 
    Journal of Scientific Computing, 63(2), 307-329, May. 2015
  • Convergence analysis for iterative data-driven tight frame construction scheme  [PDF]
    C. Bao, H. Ji and Z. Shen,  
    Applied and Computational Harmonic Analysis (ACHA), 38(3), 510-523, May. 2015
2014
  • A convergent incoherent dictionary learning algorithm for sparse coding  [PDF]
    C. Bao, Y. Quan and H. Ji, 
    European Conference on Computer Vision (ECCV),  2014
  • Data-driven tight frame construction and image denoising  [PDF], [Code]
    J. Cai, H. Ji, Z. Shen and G. Ye, 
    Applied and Computational Harmonic Analysis, (ACHA), 37(1), 89-105, Jul. 2014
  • L0 norm based dictionary learning by proximal methods with global convergence  [PDF], [Code]
    C. Bao, H. Ji, Y. Quan and Z. Shen,
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014
  • Wavelet frame based algorithm for 3D reconstruction in electron microscopy  [PDF]
    M. Li, Z. Fan, H. Ji and Z. Shen, 
    SIAM Journal on Scientific Computing, (SIAM SISC), 36(1), B45-B69, Jan. 2014
2013
  • Fast sparsity-based orthogonal dictionary learning for image restoration  [PDF
    C. Bao, J. Cai and H. Ji, 
    International Conference on Computer Vision (ICCV), 2013
  • Recovering over/under-exposed regions of a color photograph  [PDF
    L. Hou, H. Ji and Z. Shen,
    SIAM Journal on Imaging Sciences6(4) (SIAM SIIMS), 2213–2235, Nov. 2013
  • Band-limited wavelets and framelets in low dimensions  [PDF
    L. Hou and H. Ji, 
    Journal of Fourier Analysis and Applications, (JFAA), 19(4), 731-761, Aug. 2013
  • Wavelet domain multi-fractal analysis for static and dynamic texture classification  [PDF
    H. Ji, X. Yang, H. Ling and Y. Xu, 
    IEEE Transactions on Image Processing, (IEEE TIP), 22(1), 286-299, Jan. 2013
2012
  • Scale-space texture description on SIFT-like textons  [PDF]
    Y. Xu, S. Huang, H. Ji and C. Fermuller,
     Computer Vision and Image Understanding, (CVIU), 116(9), 999-1013, Sep. 2012
  • A two-stage approach to blind spatially-varying motion deblurring  [PDF]
    H. Ji and K. Wang, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
  • Real time robust L1 tracker using accelerated proximal gradient approach  [PDF]
    C. Bao, Y. Wu, H. Ling and H. Ji, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
  • Contour based recognition  [PDF]
    Y. Xu, Y. Quan, Z. Zhang, H. Ji, M. Nishigaki, C. Fermuller and D. Dementhon, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR),  2012
  • Robust image deconvolution with an inaccurate blur kernel  [PDF], [Code]
    H. Ji and K. Wang, 
    IEEE Transactions on Image Processing (IEEE TIP), 21(4), 1624-1634, Apr. 2012
  • Image deconvolution using a characterization of sharp images in wavelet domain  [PDF]
    H. Ji, J. Li, Z. Shen, and K. Wang, 
    Applied and Computational Harmonic Analysis (ACHA), 32(2), 295-304, Mar. 2012
  • Wavelet frame based blind image inpainting  [PDF]
    B. Dong, H. Ji, J. Li, Z. Shen, 
    Applied and Computational Harmonic Analysis, (ACHA), 32(2), 268-279, Mar. 2012
  • Framelet based blind image deblurring from a single image  [PDF], [Code]
    J. Cai, H. Ji, C. Liu and Z. Shen,
    IEEE Transactions on Image Processing, (IEEE TIP),  1(2), 562-572, Feb. 2012
2011
  • Robust video restoration by joint sparse and low rank matrix approximation  [PDF], [Code]
    H. Ji, S. Huang, Z. Shen, and Y.-H. Xu, 
    SIAM Journal on Imaging Sciences  (SIAM SIIMS), 4(4), 1122-1142, Nov. 2011
  • Wavelet frame based image restoration with missing/damaged pixels  [PDF]
    H. Ji, Z. Shen and Y.-H. Xu, 
    East Asia Journal on Applied Mathematics, 1(2), 108-131, 2011
  • Dynamic texture classification using dynamic fractal analysis  [PDF]
    Y. Xu, Y. Quan, H. Lin and H. Ji, 
    International Conference on Computer Vision (ICCV), 2011
2010 and Before
  • Inpainting for compressed images  [PDF]
    J. Cai, H. Ji, F. Shang and Z. Shen, 
    Applied and Computational Harmonic Analysis (ACHA), 29(3), 368-381, Nov. 2010
  • Learning shift-invariant sparse representation of actions  [PDF]
    Y. Li, C. Fermuller, Y. Aloimonos and H. Ji, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010
  • A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid  [PDF]
    Y. Xu, X. Yang, H. Ling and H. Ji, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010
  • Robust video denoising using low rank matrix completion  [PDF]
    H. Ji, C. Liu, Z. Shen and Y.-H. Xu, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010
  • Wavelet frame based scene reconstruction from range data  [PDF]
    H. Ji, Z. Shen and Y.-H. Xu, 
    Journal of Computational Physics229 (JCP), (6), 2093-2018, Mar. 2010
  • Illusory motion due to causal time filtering  [PDF]
    C. Fermuller, H. Ji and A. Kitaoka, 
    Vision Research, 50 (3), 315-329, Feb. 2010
  • Blind motion deblurring using multiple images  [PDF]
    J. Cai, H. Ji, C. Liu and Z. Shen, 
    Journal of Computational Physics (JCP), 228 (14), 5057-5071, Aug. 2009
  • Combining powerful local and global statistics for texture description  [PDF]
    Y. Xu, S. Huang, H. Ji and C. Fermuller, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009
  • Blind motion deblurring from a single image using sparse approximation  [PDF], [Code]
    J. Cai, H. Ji, C. Liu and Z. Shen, 
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009
  • High-quality curvelet-based motion deblurring using an image pair  [PDF]
    J. Cai, H. Ji, C. Liu and Z. Shen, 
    IEEE Conference Computer Vision and Pattern Recognition (CVPR), 2009
  • Viewpoint invariant texture description using fractal analysis  [PDF]
    Y. Xu, H. Ji and C. Fermuller, 
    International Journal of Computer Vision (IJCV), 83 (1), 85-100, Jun. 2009
  • Compactly supported orthonormal complex wavelets with dilation four and symmetry  [PDF]
    B. Han and H. Ji, 
    Applied and Computational Harmonic Analysis (ACHA), 26, 422-431, May 2009
  • Robust wavelet-based super-resolution reconstruction: Theory and Algorithm  [PDF]
    H. Ji and C. Fermuller,
    IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE PAMI), 31(4), 649-660, Apr. 2009
  • Motion blur identification from image gradients  [PDF
    H. Ji and C. Liu,
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008
  • Noise cause slant underestimation in motion and stereo  [Download
    H. Ji, C. Fermuller,
    Vision Research, 46 (19), 3105-3120, Aug. 2006
  • A 3D shape constraint on video  [Download
    H. Ji, C. Fermuller,
    IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE PAMI), 28(6), 1018-1023, Jun. 2006
  • Super-resolution reconstruction from extended video sequences,  [PDF
    H. Ji, C. Fermuller,
    European Conference on Computer Vision (ECCV), 2006
  • A projective invariant for textures  [PDF
    Y. Xu, H. Ji and C. Fermuller,
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, 2006
  • Integration of motion fields through shape  [PDF
    H. Ji and C. Fermuller,
    IEEE Conference Computer Vision and Pattern Recognition (CVPR),San Diego, 2005
  • Bias in shape estimation  [Link
    H. Ji and C. Fermuller,
    European Conference on Computer Vision (ECCV), 405-416, Czech, 2004
  • Compactly supported (bi) orthogonal wavelets generated by interpolatory refinable functions  [PDF
    H. Ji and Z. Shen,
    Advances in Computational Mathematics,11 (1), 81-104, 1999
  • Multivariate compactly supported fundamental refinable functions, duals, and biorthogonal wavelets  [PDF
    H Ji, SD Riemenschneider, Z Shen,
    Studies in Applied Mathematics 102 (2), 173-204, 1999

 

 

 

 

 

 

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