zhao yong qiang


http://teacher.nwpu.edu.cn/en/zhaoyq

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The basic information

zhao yong qiang

School of Automation

Postgraduate

Doctor

Professor

Admission Information

Scientific Research

Research Directions:

Image processing, Computer vision, Polarization Imaging,  Hyperspectral Remote Sensing, 

Pattern Recognition, Photoelectric Detection and so on.

(1) Bionic polarization vision

Polarization vision, which could accurately obtain all kinds of information of the interested targets in scattering medium, is a new research direction in the field of machine vision. The research work is mainly on carrying out high-speed and high-resolution micro-nano grating array polarization imaging, multiband polarization joint encoding and decoding, bionic multiband polarization visual model, multiband polarization visual information analysis, bionic polarization light integrated navigation and so on.

(2) High-dimensional image reconstruction

Obtaining images with high-spatial, spectrum, and polarization resolution through post-processing is one of the important development directions of spectropolarimetric remote sensing, which also brings new challenges to the theory of traditional image reconstruction. And in this field, researches on compressed spectropolarimetric imaging combined with sparse sampling, multi-band image super-resolution reconstructions, computing spectral imaging, multi-objective joint optimization and so on have been carried out.

(3) Multi-band photoelectric detection system

Carrying out research work mainly on different directions such as target detection, tracking, recognition of visible light, infrared, multispectral and polarized image, and designing different imaging system combined with engineering practice.

Research projects

(1)

National Natural Science Foundation of China  (NSFC:61771391), “Spectropolarimetric Imaging Based on Gratting Array and Deep Learning”. 1/2018 —12/2021(principal investigator)

(2)Joint research project between National Natural Science Foundation of China and Korea National Research Foundation (NSFC-NRF: 61511140292), “The theoretical research on multi-band polarization imaging based on micro-nano filter cascaded array ”. 7 2015—6/2017 (principal investigator)

(3) National Natural Science Foundation of China (NSFC: 61371152), “The theoretical research on joint sparse sampling applied to compressed spectropolarimetric imaging”. 1/2014—12/2017 (principal investigator)

(4) National Natural Science Foundation of China (NSFC: 61071172), “Research on multi-band bionic polarized visual sensing model”. 1/2011—12/2013 (principal investigator)  

(5) National Natural Science Foundation of China (NSFC: 60602056), “Synthesis and application of imaging spectropolarimetric detected information using multi-scale analysis”. 1/2007—12/2009 (principal investigator)

Academic Achievements

 


Books:

[1] Yongqiang Zhao, Quan Pan,S.G.Kong, Chen Yi, Yongmei Cheng. Multi-band Optical Polarization Imaging and Application. Springer-NDIP.2015.

[2] Yongqiang Zhao, Quan Pan,Yongmei Cheng. Imaging Spectropolarimetric Remote Sensing and Application. NDIP. 2011.

Papers(Part codes can be download from http://github.com/polwork/):

[1] X. Kong, Y.Zhao, J. Xue, J. C-W Chan. Destripe of Hyperspectral Images by Global and Local Tensor Sparse Approximation Model. Submitted.

[2]J. Hao,Y. Zhao,J. C-W Chan, S.G. Kong.A Design of Micro-polarizer Array for High-precision Polarization Imaging. Submitted.

[3]Y. Zhao, M. Wang, and J. C-W Chan. Hyperspectral Snapshot Imaging via Spectral-Filter Array 3D Residual Demosaicking Network. Submitted.

[4]M. Reda, Y. Zhao and J. C-W Chan. Matching enhancement using polarization and depth information.Submitted. 

[5] L. Shen, M. Reda,Y.Zhao, Image Enhancement of Shadow Region Based on Polarization Imaging,Submitted.

[6]L. Chen, Y. Zhao, S.G. Kong, et al. A large-scale Hyperspectral videos dataset and spatial-spectral feature-based tracking benchmark,Submitted.

[7]Y. Zhao, M. Reda, S.G. Kong, et al. Detecting GCTB Lesions using Mueller Matrix Polarization Microscopic Imaging and Multi-parameters Fusion Network, Submitted.

[8]X. Kong, Y.Zhao, J. Xue, J. C-W Chan. Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation. Remote Sensing. 2019.

[9]J. Xue,Y. Zhao, W. Liao et al. Nonconvex tensor rank minimization and its applications to tensor recovery.Information Sciences.2019. 503 (2019) 109128.

[10]J. Xue,Y. Zhao, W. Liao et al. Hyper-Laplacian Regularized Nonlocal Low-rank Matrix Recovery for Hyperspectral Image Compressive Sensing Reconstruction. Information Sciences.2019. 501 (2019) 406420

[11]C. Yi,Y. Zhao, J.W. Chan. Spectral super-resolution for multispectral image based on spectral improvement strategy and spatial preservation strategy.IEEE Trans. Geoscience and Remote Sensing. 2019.

[12]J. Yang,Y. Zhao,J.C-W Chan, Multi-Scale Wavelet 3D-CNN Based Hyperspectral Image Super-Resolution. Remote Sensing.2019.  

[13]J. Xue, Y. Zhao, W. Liao, J.C-W Chan. Nonlocal Low-rank Regularized Tensor Decomposition for Hyperspectral Image Denoising, IEEE Trans. Geoscience and Remote Sensing. 2019. 57(7):5174 - 5189.

[14]J. Xue, Y. Zhao, W. Liao, J.C-W Chan.Nonlocal Tensor Sparse Representation and Low-Rank Regularization for Hyperspectral Image Compressive Sensing Reconstruction. Remote Sensing.2019,11(2).

[15]N.Li, Y.Zhao, Q. Pan, S.G.Kong, Demosaicking DoFP Images Using Newton's Polynomial Interpolation and Polarization Difference Model. Optical Express, 27(2):1376-1391, 2019.  

[16]J. Xue,Y. Zhao and W. Liao et al. Total Variation and Rank-1 Constraint RPCA for Background Subtraction. IEEE Access. 6:49955 - 49966, 2018  

[17]L. Shen, Y. Zhao,Q. Peng, J.C-W Chan, S.G.Kong.An Iterative Image Dehazing Method With Polarization. IEEE Trans. On Multimedia. 2019.   

[18]N. Li, Y. Zhao, Q. Pan, S.G.Kong.Removal of reflections in LWIR image with polarization characteristics.Optical Express.26(13):16488-16504, 2018.  

[19]J. Yang, Y. Zhao,J. C.-W. Chan. Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network. Remote Sensing, 10(5):800-812. 2018.   

[20]C. Yi,Y. Zhao, J.W. Chan. Hyperspectral image super-resolution based on spatial and spectral correlation fusion. IEEE Trans. Geoscience and Remote Sensing. 65(7):4165 - 4177, 2018.   

[21]Y. Zhao, M. Wang, J.W. Chan. FOV Expansion of Bioinspired Multiband Polarimetric Imagers With Convolutional Neural Networks. IEEE Photonics Journal.10(1):1-10, 2018.   

[22]J. Xue, Y. Zhao, W. Liao, and S. G. Kong.Joint Spatial and Spectral Low-Rank Regularization for Hyperspectral Image Denoising. IEEE Trans. Geoscience and Remote Sensing.56(4):1940 - 1958, 2018.  

[23]L. Li, Y. Zhao, J. Sun, et. al. Deformable Dictionary Learning for SAR Image Change Detection. IEEE Trans. Geoscience and Remote Sensing. 56(8): 4605 - 4617, 2018.   

[24]X. Ping, Y. Liu, Y. Zhao et al. 3-D reconstruction of textureless and high-reflective target by polarization and binocular stereo vision. Journal of Infrared and Millimeter Waves,36(4):432-438, 2017.

[25]M. Reda, Y. Zhao and J. C-W Chan. Polarization guided autoregressive model for depth recovery. IEEE Photonics Journal. 9(3):1-10, 2017.

[26]J. Yang, Y. Zhao, J. C-W Chan. Learning and Transferring Deep Joint Spectral- Spatial Feature for Hyperspectral Classification. IEEE Trans. Geoscience and Remote Sensing. 55(8):4729 - 4742, 2017.

[27]J. Yang, Y. Zhao, C. Yi and J. C-W Chan. No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning. Remote Sensing. 2017. 

[28]C. Yi, Y. Zhao, J. Yang, J. C-W Chan, and Seong G. Kong, Joint Hyperspectral Superresolution and Unmixing With Interactive Feedback. IEEE Trans. Geoscience and Remote Sensing. 55(7):3823 - 3834, 2017.

[29]J. Yang, Y. Zhao, J. C-W Chan and S.G. Kong. Coupled Sparse Denoising and Unmixing with Low Rank Constraint for Hyperspectral Image. IEEE Trans. Geoscience and Remote Sensing. 54(3):1818 - 1833,2016.

[30]J. Wen, J. E. Fowler, M. He, Y. Zhao, C. Deng, and V. Menon. Orthogonal Nonnegative Matrix Factorization Combining Multiple Features for Hyerspectral Image Spectral-Spatial Dimension Reduction.IEEE Trans. Geoscience and Remote Sensing. 54(7):4272 - 4286, 2016.

[31]Y. Zhao, Q. Peng, C. Yi and S. G. Kong. Multi-band Polarization Imaging. Journal of Sensors. 2016.

[32]Y. Zhao, J. Yang. Hyperspectral Image Denoising via Sparse Representation and Low-Rank Constraint. IEEE Trans. on Geoscience and Remote Sensing, 53(1):296- 308, 2015.

[33] Y. Zhao, J. Yang, JCW Chan. Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint Nonlocal Similarity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):1-13, 2014.

[34]S.Gao, Y. Cheng, Y. Zhao et al. Data-driven quadratic correlation filter using sparse coding for infrared targets detection. Journal of Infrared and Millimeter Waves. 33(5):498-506,2014.

[35]S. Gao, Y. Cheng, Y. Zhao. Method of visual and infrared fusion for moving object detection. Optics letters, 38 (11):1981-1983, 2013.

[36]Y. Zhao, S. G. Kong. Automated classification of touching or overlapping M-FISH chromosomes by region fusion and homolog pairing. Pattern Analysis and Applications, 16 (1):31-39, 2013.

[37]S. Gao, Y. Cheng, Y. Zhao. Unsupervised change detection of satellite images using low rank matrix completion. Optics Letters, 38 (23):5146-5149,2013.

[38]J. Wen, Y. Zhao, X. Zhang, W. Yan, W. Lin, Local discriminant nonnegative matrix factorization feature extraction for hyperspectral image classification, International Journal of Remote Sensing, 35(13): 5073–5093, 2014.

[39]Y. Zhao, Q. Zhang, J. Yang. High-resolution multiband polarization epithelial tissue imaging method by sparse representation and fusion. Applied Optics, 51 (4):A27-A35, 2012.

[40]Y. Zhao, L. Zhang, S.G. Kong. Band-subset-based clustering and fusion for hyperspectral imagery classification. IEEE Trans. on Geoscience and Remote Sensing, 49 (2):747-756, 2011.

[41]Y. Zhao, G. Zhang, F. Jie, S. Gao, C. Chen, Q. Pan. Unsupervised Classification of Spectropolarimetric Data by Region-Based Evidence Fusion. IEEE Geoscience and Remote Sensing Letters, 2011:755-759.

[42]Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, Q. Pan. Hyperspectral imagery super-resolution by sparse representation and spectral regularization. EURASIP Journal on Advances in Signal Processing, 2011 (1):87.

[43]Y. Zhao, X. Wu, S.G. Kong, L. Zhang. Joint segmentation and pairing of multispectral chromosome images. Pattern Analysis & Applications, 2011:1-10.

[44]Y. Wu, Y.M. Cheng, Y.Q. Zhao et al. Infrared target detection using kernel Rayleigh quotient quadratic correlation filter. Journal of Infrared and Millimeter Waves. 30(2):142-148,2011.

[45]C. Chen, Y.Q. Zhao, L. Luo et al. Model and Analysis of Spectropolarimetric BRDF of Painted Target Based on GA-LM Method. Spectroscopy and Spectral Analysis. 2010: 729-734.

[46]Y. Zhao, P. Gong, Q. Pan. Object Detection by Spectropolarieteric Imagery Fusion, IEEE Trans. on Geoscience and Remote Sensing, 46(9), 2008.

[47]Y. Zhao, L. Zhang, Q. Pan. Spectropolarimetric imaging for pathological analysis of skin, Applied Optics, 48(10): D236-D246, 2009.

[48]Y. Zhao, L. Zhang, Q. Pan. Object Separation by Polarimetric and Spectral Imagery Fusion, Computer Vision and Image Understanding, 2009.

[49]S.B.Gao, Y.M. Cheng, Y.Q. Zhao et al. Detection of Buried Target Based on Multitemporal Infrared Image. Journal of Infrared and Millimeter Waves. 28(1):142-148, 2009.

[50]Y. Zhao, P. Gong, Q. Pan, Unsupervised spectropolarimetric imagery clustering fusion, Journal of Applied Remote Sensing, 3(1), 033535, 2009.

[51]Y. Zhao, Q. Pan, H. Zhang. New Polarization Imaging Method based on Spatially Adaptive Wavelet Image Fusion, Optical Engineering,45(12): 123202 -1-6, 2006.

 

 

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