Research Codes

CVPR 2013

[1]         W. Zuo, L. Zhang, C. Song and D. Zhang, “Texture Enhanced Image Denoising via Gradient Histogram Preservation,” in CVPR 2013. (paper) (code)

[2]         W. Xue, L. Zhang and X. Mou, “Learning without Human Scores for Blind Image Quality Assessment,” in CVPR 2013. (paper) (code)

 

AAAI 2013

[3]         D. Meng, Z. Xu, L. Zhang and J. Zhao, “A Cyclic Weighted Median Method forL₁ Low-Rank Matrix Factorization with Missing Entries,” inAAAI 2013. (paper) (code)(A very simple but very efficient and effective L₁ matrix factorization algorithm.)

 

ECCV 2012

[4]         K. Zhang, L. Zhang and M.H. Yang, “Real-time Compressive Tracking,” in ECCV 2012.(paper) (code and website)

(No training, no feature selection, speed up-to 40fps under Matlab, but with state-of-the-art tracking performance in terms of both success rate and center location error!)

[5]         B. Peng, L. Zhang, “Evaluation of Image Segmentation Quality by Adaptive Ground Truth Composition,” in ECCV 2012. (paper) (code and website)

(A novel metric to evaluate the quality of image segmentation!)

[6]         W. Lian and L. Zhang, “Robust Point Matching Revisited: A Concave Optimization Approach,” in ECCV 2012. (paper) (code)

[7]         M. Yang, L. Zhang and D. Zhang, “Efficient Misalignment-Robust Representation for Real-Time Face Recognition,” in ECCV 2012. (paper) (code)

[8]         P. Zhu, L. Zhang, Q. Hu and Simon C.K. Shiu,“Multi-scale Patch based Collaborative Representation for Face Recognition with Margin Distribution Optimization,” in ECCV 2012. (paper) (code)

 

CVPR 2012:

[9]         M. Yang, L. Zhang, D. Zhang and S. Wang, “RelaxedCollaborative Representation for Pattern Classification,” inCVPR 2012. (paper) (code)

[10]     S. Wang, L. Zhang, Y. Liang and Q. Pan, “Semi-Coupled Dictionary Learning with Applications to Image Super-Resolution and Photo-Sketch Image Synthesis,” inCVPR 2012. (paper) (code and website)

 

ICCV 2011:

[11]     L. Zhang, M. Yang and X. Feng, “Sparse Representation or Collaborative Representation: Which Helps Face Recognition?” inICCV 2011. (paper,code)

[12]     M. Yang, L. Zhang, X. Feng and D. Zhang, “Fisher Discrimination Dictionary Learning for Sparse Representation,” inICCV 2011. (paper,code)

[13]     L. Zhang, P. Zhu, Q. Hu and D. Zhang, “A Linear Subspace Learning Approach via Sparse Coding,” inICCV 2011. (paper,code)(Some errors in the experimental results on MPIE database were corrected.)

[14]     W. Dong, L. Zhang and G. Shi, “Centralized Sparse Representation for Image Restoration,” inICCV 2011. (paper,code)

 

CVPR 2011:

[15]     Meng Yang, Lei Zhang, Jian Yang and David Zhang, “Robust Sparse Coding for Face Recognition,” CVPR 2011.(paper) (code)

     This work presents a general sparse coding method which can handle the face occlusion and corruption very effectively!

[16]     Weisheng Dong, Xin Li, Lei Zhang and Guangming Shi, “Sparsity-based Image Denoising via Dictionary Learning and Structural Clustering,” CVPR 2011 (oral).(paper) (code)

 

ECCV 2010:

[17]     M. Yang and L. Zhang, “Gabor Feature based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary,”ECCV 2010. (code)

[18]     W. Lian and Lei Zhang, “Rotation invariant non-rigid shape matching in cluttered scenes,”ECCV 2010. (code)

 

Image Quality Assessment

[19]     Lin Zhang, Lei Zhang, X. Mou and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment,”IEEE Trans. Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011.(paper,website & code)

[20]     Lin Zhang, Lei Zhang and Xuanqin Mou, “RFSIM: A FEATURE BASED IMAGE QUALITY ASSESSMENT METRIC USING RIESZ TRANSFORMS,”ICIP 2010. (code)

 

Image Interpolation, Restoration and Video Super-resolution

[21]     W. Dong, L. Zhang, G. Shi and X. Li, “Nonlocally Centralized Sparse Representation for Image Restoration”,IEEE Trans. on Image Processing, vol. 22, no. 4, pp. 1620-1630, Apr. 2013.(paper)(website) (code)

(This paper is an improvement of our ICCV11 paper “Centralized Sparse Representation for Image Restoration”. The new model is simpler and more efficient.)

[22]     W. Dong, L. Zhang, R. Lukac, G. Shi, “Sparse Representation based Image Interpolation with Nonlocal Autoregressive Modeling”,IEEE Trans. on Image Processing, vol. 22, no. 4, pp. 1382-1394, Apr. 2013.(paper)(website) (code)

[23]     W. Dong, L. Zhang, G. Shi and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” accepted inIEEE Trans. on Image Processing. (paper,matlab  code & website)

[24]     L. Zhang and X. Wu, “An edge-guided image interpolation algorithm via directional filtering and data fusion,”IEEE Trans. on Image Processing, vol. 15, pp. 2226-2238, Aug. 2006. (paper,matlab code)

[25]     Weisheng Dong, Lei Zhang, Guangming Shi, Xiaolin Wu, “NONLOCAL BACK-PROJECTION FOR ADAPTIVE IMAGE ENLARGEMENT”,ICIP 2009. (code)(a small bug has been fixed)

[26]     L. Zhang, X. Li and D. Zhang, “Image Denoising and Zooming under the LMMSE Framework,” to appear inIET Image Processing. (paper) (code)

[27]     Huihui Song, Lei Zhang, Peikang Wang, Kaihua Zhang and Xin Li, “AN ADAPTIVE L1-L2 HYBRID ERROR MODEL TO SUPER-RESOLUTION,” ICIP 2010.(code)

 

Image Segmentation

[28]     K. Zhang, L. Zhang, H. Song, and D. Zhang, “Re-initialization Free Level Set Evolution via Reaction Diffusion.” IEEE Trans. on Image Processing, to appear. (paper)(code and website)

(This work unifies the level set evolution under the reaction diffusion framework, which is completely free of re-initialization.)

[29]     B. Peng, L. Zhang and D. Zhang, “Automatic Image Segmentation by Dynamic Region Merging”,IEEE Trans. Image Processing, vol. 12, no. 12, pp. 3592-3605, 2011. (paper,software, website) (Source code)

[30]     B. Peng, L. Zhang, D. Zhang and J. Yang, “Image Segmentation by Iterated Region Merging with Localized Graph Cuts”, to appear inPattern Recognition. (paper) (software)

[31]     K. Zhang, L. Zhang, H. Song and W. Zhou, “Active contours with selective local or global segmentation: a new formulation and level set method,”Image and Vision Computing, vol. 28, issue 4, pp. 668-676, April 2010. (paper,matlab code,website)

[32]     K. Zhang, H. Song and L. Zhang, “Active Contours Driven by Local Image Fitting Energy,”Pattern recognition, vol. 43, issue 4, pp. 1199-1206, April 2010. (paper,matlab code)

[33]     J. Ning, L. Zhang, D. Zhang and C. Wu, “Interactive Image Segmentation by Maximal Similarity based Region Merging,”Pattern Recognition, vol. 43, pp. 445-456, Feb, 2010. (paper,website & code)

[34]     Kaihua Zhang, Lei Zhang and Su Zhang, “A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction,”ICIP 2010. (code)

[35]     Bo Peng, Lei Zhang and Jian Yang, “Iterated Graph Cuts for Image Segmentation,” ACCV 2009. (software)

 

Image Denoising

[36]     L. Zhang, W. Dong, D. Zhang and G. Shi, “Two-stage Image Denoising by Principal Component Analysis with Local Pixel Grouping,”Pattern Recognition, vol. 43, issue 4, pp. 1531-1549, April 2010. (paper,matlab code,website)(Code optimized!)

[37]     L. Zhang, B. Paul and X. Wu, “Multiscale LMMSE-based image denoising with optimal wavelet selection,”IEEE Trans. on Circuits and Systems for Video Technology, vol. 15, pp. 469-481, April 2005.(paper,matlab code)

 

Color Demosaicking and Joint Demosaicking-Denoising, etc

[38]     L. Zhang, X. Wu, A. Buades, and X. Li, "Color Demosaicking by Local Directional Interpolation and Non-local Adaptive Thresholding," Journal of Electronic Imaging 20(2), 023016 (Apr-Jun 2011), DOI:10.1117/1.3600632.(paper,website and dataset, code)

[39]     Lei Zhang, Weisheng Dong, Xiaolin Wu, Guangming Shi, “Spatial-Temporal Color Video Reconstruction from Noisy CFA Sequence,”IEEE Trans. on Circuits and Systems for Video Technology, vol. 20, no. 6, pp. 838-847, June 2010.(paper,website, source code will be available soon)

[40]     L. Zhang,R. Lukac, X. Wu and D. Zhang, “PCA-based Spatially Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras,”IEEE Trans. on Image Processing, vol. 18, no. 4, pp. 797-812, April 2009.(paper,matlab code,website)

[41]     L. Zhang, X. Wu and D. Zhang, “Color Reproduction from Noisy CFA Data of Single Sensor Digital Cameras,”IEEE Trans. Image Processing, vol. 16, no. 9, pp. 2184-2197, Sept. 2007. (paper,matlab code,website)

[42]     L. Zhang and X. Wu, “Color demosaicking via directional linear minimum mean square-error estimation,”IEEE Trans. on Image Processing, vol. 14, pp. 2167-2178, Dec. 2005. (paper,matlab code)

[43]     L. Zhang and D. Zhang, “A joint demosaicking-zooming scheme for single chip digital color cameras”,Computer Vision and Image  Understanding, Special issue on Color Image Processing, vol. 107, pp. 14-25, July-Aug, 2007.(paper,matlab code)

[44]     X. Wu and L. Zhang, “Improvement of color video demosaicking in temporal domain,”IEEE Trans. on Image Processing, vol. 15, pp. 3138-3151, Oct. 2006. (paper,software)

 

Edge Detection and Corner Detection

[45]     B. Paul, L. Zhang and X. Wu, “Canny edge detection enhancement by scale multiplication,”IEEE. Trans. on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1485-1490, Sept. 2005.(paper,matlab code)

[46]     Lin Zhang, Lei Zhang and D. Zhang, “A Multi-Scale Bilateral Structure Tensor Based Corner Detector,” ACCV 2009.(code)

 

Point Set Matching and Shape Matching

[47]     W. Lian, L. Zhang and D. Zhang, “Rotation Invariant Nonrigid Point Set Matching in Cluttered Scenes,”IEEE Trans. Image Processing, 2012. (paper,source code)

[48]     W. Lian,L. Zhang, Y. Liang and Q. Pan, “A Quadratic Programming based Cluster Correspondence Projection Algorithm for Fast Point Matching,”Computer Vision and Image Understanding, Vol. 114, Issue 3, pp. 322-333, March 2010.(paper,matlab code)

 

Object Tracking

[49]     J. Ning, L. Zhang, D. Zhang and C. Wu, “Scale and Orientation Adaptive Mean Shift Tracking,” to appear inIET Computer Vision. (paper,website.matlab code)

[50]     J. Ning, L. Zhang, D. Zhang and C. Wu, “Robust Mean Shift Tracking with Corrected Background-Weighted Histogram,” to appear inIET Computer Vision. (paper,website.matlab code)(We prove that the background-weighted histogram in the original mean-shift tracking method is incorrect.)

[51]     J. Ning, L. Zhang, D. Zhang and C. Wu, “Robust Object Tracking using Joint Color-Texture Histogram,”International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, No. 7 (2009) 1245–1263.(paper,matlab code)

 

Texture Classification

[52]     Z. Guo, L. Zhang and D. Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification,”IEEE Trans. on Image Processing, vol. 19, no. 6, pp. 1657-1663, June 2010.(paper,matlab code)(a small bug in the code is fixed)

[53]     Z. Guo, L. Zhang and D. Zhang, “Rotation Invariant Texture Classification using LBP Variance (LBPV) with Global Matching”,Pattern Recognition, vol. 43, no. 3, pp. 706-719, Mar. 2010. (paper,matlab code)

[54]     Lin Zhang, Lei Zhang, Zhenhua Guo and David Zhang, “MONOGENIC-LBP: A NEW APPROACH FOR ROTATION INVARIANT TEXTURE CLASSIFICATION,”ICIP 2010. (code)

[55]     Zhenhua Guo, Lei Zhang, David Zhang and Su Zhang, “Rotation Invariant Texture Classification Using Adaptive LBP with Directional Statistical Features,”ICIP 2010. (code)

 

Image Retrieval

[56]     Guang-Hai Liu, Lei Zhang,Ying-Kun Hou, Zuo-yong Li and Jing-Yu Yang, “Image Retrieval Based on Multi-Texton Histogram,” accepted byPattern Recognition. (paper,code)

[57]     G. Liu, Z. Li, L. Zhang and Y. Xu, “Image Retrieval based on Micro-structure Descriptor,” to appear inPattern Recognition. (paper) (code)

 

Face Recognition

[58]     M. Yang, L. Zhang, J. Yang and D. Zhang, “Regularized Robust Coding for Face Recognition.”IEEE Trans. on Image Processing, to appear. (paper) (code)

[59]     M. Yang, L. Zhang, S. Shiu, and D. Zhang, “Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition,”IEEE Trans. on Information Forensics and Security, vol. 7, no. 6, pp. 1738-1751, Dec. 2012.(paper) (code)

[60]     Lei Zhang, Meng Yang, Zhizhao Feng and David Zhang. On the Dimensionality Reduction for Sparse Representation based Face Recognition.ICPR 2010. (paper) (code).

[61]     Meng Yang, Lei Zhang, Daivd Zhang and Jian Yang, “Metaface Learning for Sparse Representation based Face Recognition,”ICIP 2010. (code)

[62]     Z. Guo, L. Zhang, D. Zhang and X.Q. Mou, “HIERARCHICAL MULTISCALE LBP FOR FACE AND PALMPRINT RECOGNITION,” ICIP 2010. (code)

[63]     Q. Gao, L. Zhang and D. Zhang, “Face Recognition using FLDA with Single Training Image Per-person,”Applied Mathematics and Computation, vol. 205, pp. 726-734, 2008. (paper,code)

 

Medical Image Processing and Enhancement

[64]     B. Paul and L. Zhang “Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding,”IEEE Trans. on Medical Imaging, vol.22, pp. 1089-1099, Sep. 2003. (paper,matlab code)

[65]     Bob Zhang, Lin Zhang, Lei Zhang and Fakhri Karray, “Retinal Vessel Extraction by Matched Filter with First-Order Derivative of Gaussian,”Computers in Biology and Medicine, Volume 40, Issue 4, April 2010, Pages 438-445. (paper,matlab code)

 

Bioinformatics

[66]     C. Zheng, L. Zhang, T. Ng, C. Shiu and D. Huang, “Molecular Pattern Discovery Based on Penalized Matrix Decomposition”, accepted byIEEE/ACM Transactions on Computational Biology and Bioinformatics. (paper,code)

[67]     C. Zheng, L. Zhang, T. Ng, and C. Shiu, “Metasample Based Sparse Representation for Tumor Classification,” accepted byIEEE/ACM Transactions on Computational Biology and Bioinformatics. (paper,code)

[68]     C. Zheng, D. Huang, L. Zhang and X. Kong, “Tumor Clustering Using Nonnegative Matrix Factorization with Gene Selection,”IEEE Trans. Information Technology in Biomedicine, vol. 13, no. 4, pp.599-607, July 2009.(paper,code)

 

 

http://www4.comp.polyu.edu.hk/~cslzhang/code.htm

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