Publication

Upcoming

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), 2025

2025

Image shadow removal via multi-scale deep Retinex decomposition
X. Lu, Y. Quan, Y. Huang, Y. Xu, H. Ji
Pattern Recognition (PR), 159, 111126, Mar., 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
Multi-focus image fusion via explicit defocus blur modelling [PDF]
Y. Quan, X. Wan, Z. Tang, J. Liang, and H. Ji,
39th AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, Feb, 2025

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 [PDF],
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), 46 (12), 11361-11377, Dec. 2024
Test-time model adaption for image reconstruction using self-supervised adaptive layers [PDF] [GitHub]
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