Publication

Upcoming

Image shadow removal via multi-scale deep Retinex decomposition
X. Lu, Y. Quan, Y. Huang, Y. Xu, H. Ji
Pattern Recognition (PR), 2024
Cross-scale self-supervised blind image deblurring via implicit neural representation [PDF] [GitHub],
T. Zhang, Y. Quan, and H. Ji,
Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
Pseudo-Siamese directional transformers for Self-supervised real-world denoising,
Y. Quan, T. Zheng, and H. Ji,
Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
Deep single image defocus deblurring via Gaussian kernel mixture learning [PDF],
Y. Quan, Z. Wu, R. Xu, H. Ji,
IEEE Transactions on Pattern Recognition and Machine Intelligence (IEEE TPAMI), 2024
Dual-path deep unsupervised learning for multi-focus image fusion [PDF],
Y. Quan, T. Zheng, Y. Huang, and H. Ji,
IEEE Transactions on Multimedias (IEEE TMM), 2024

2025

Phase unwrapping via fully exploiting global and local spatial dependencies [PDF],
Y. Quan, X. Yao, Z. Chen, and H. Ji,
Optics and Laser Technology, 181, Part B, 111872, Feb., 2025

2024

Test-time model adaption for image reconstruction using self-supervised adaptive layers [PDF]
Y. Zhao, T. Zhang, and H. Ji
European Conference on Computer Vision (ECCV), MiCo Milano, Oct., 2024
Enhancing underwater images via asymmetric multi-scale invertible networks [PDF]
Y. Quan, X. Tan, Y. Huang, Y. Xu, and H. Ji
ACM Multimedia (ACM MM), Melbourne, Oct., 2024
Text-guided portrait image matting [PDF],
Y. Xu, X. Yao, B. Liu, Y. Quan, and H. Ji,
IEEE Transactions on Artificial Intelligence (IEEE TAI), 5, 4149-4162, Aug., 2024
Siamese cooperative learning for unsupervised image reconstruction from incomplete measurements [PDF], [GitHub]
Y. Quan, X. Qin, T. Pang, and H. Ji,
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 46 (7), 4866-4879, Jul, 2024
Unsupervised deep unrolling network for phase unwrapping [PDF], [GitHub]
Z. Chen, Y. Quan, and H. Ji,
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Jun., 2024
Enhanced Deep Unrolling Networks for Snapshot Compressive Hyperspectral Imaging [PDF],
X. Qin, Y. Quan, and H. Ji,
Neural Networks,174, 106250, Jun., 2024

2023

Self-supervised deep learning for image reconstruction: A Langevin Monte Carlo approach [PDF],
J. Li, W. Wang and H. Ji,
SIAM Journal on Imaging Sciences (SIAM SIIMS), 16(4), 2247-2284, 2023
Single image defocus deblurring via implicit neural inverse kernels [PDF], [GitHub]
Y. Quan, X. Yao, and H. Ji,
International Conference on Computer Vision (ICCV), Paris, Oct., 2023
Fingerprinting deep image restoration models [PDF], [GitHub]
Y. Quan, H. Teng, R. Xu, J. Huang, and H. Ji,
IEEE/CVF International Conference on Computer Vision (ICCV), Paris, Oct., 2023
Image de-snowing via deep invertible separation [PDF], [GitHub]
Y. Quan, X. Tan, Y. Huang, Y. Xu and H. Ji,
IEEE/CVF IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 33(7), 3133-3144, Jul., 2023
Self-supervised blind motion deblurring with deep expectation maximization [PDF], [GitHub]
J. Li, W. Wang, Y. Nan, and H. Ji,
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Jun., 2023
Ground-truth free meta-learning for deep compressive sampling [PDF]
X. Qin, Y. Quan, T. Pang, and H. Ji,
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Jun., 2023
Neumann network with recursive kernels for single image defocus deblurring [PDF]
Y. Quan, Z. Wu, H. Ji,
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Jun., 2023
Unsupervised deep video denoising with untrained network [PDF]
H. Zheng, T. Pang, and H. Ji,
37th AAAI Conference on Artificial Intelligence (AAAI), Washington DC, Feb, 2023
Unsupervised deep learning for phase retrieval via teacher-student distillation [PDF], [GitHub],
Y. Quan, Z. Chen, T. Pang, and H. Ji,
37th AAAI Conference on Artificial Intelligence (AAAI), Washington DC, Feb, 2023
Self-supervised blind image deconvolution via deep generative ensemble learning [PDF]
M. Chen, Y. Quan, Y. Xu and H. Ji,
IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 33(2), 634–647, 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 (IEEE TNNLS), 33(10), 5387-5400, Oct. 2022
Dual-domain self-supervised learning and model adaption for deep compressive imaging [PDF], [GitHub]
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], [GitHub]
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] [GitHub]
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

2011–2015

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
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
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
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
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