Google Scholar Profile

GitHub page

Publications

  • Jing Qin and Weihong Guo (2021): Two-stage Geometric Information Guided Compressive Imaging, Advances in Data Science (accepted) [PDF]
  • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2021): A Simple Recovery Framework for Signals with Time-Varying Sparse Support, Advances in Data Science (accepted)
  • Xuemei Chen and Jing Qin (2021): Regularized Kaczmarz Algorithms for Tensor Recovery (submitted). [Preprint]
  • Igor Yanovsky and Jing Qin (2021): Spatio-Temporal Super-Resolution Reconstruction of Remote Sensing Data (submitted).
  • Weihong Guo, Yifei Lou, Jing Qin, Ming Yan (2021): A Novel Regularization Based on the Error Function for Sparse Recovery, Journal of Scientific Computing (accepted). [Preprint]
  • Jing Qin, Shuang Li, Deanna Needell, Anna Ma, Rachel Grotheer, Chenxi Huang, and Natalie Durgin (2021): Stochastic Greedy Algorithms for Multiple Measurement Vectors, Inverse Problems & Imaging, 15(1):79-107. [Link] [PDF]
  • Jing Qin and Igor Yanovsky (2020): An Effective Super-Resolution Reconstruction Method for Geometrically Deformed Image Sequences, 16th Specialist Meeting on Microwave Radiometry and Remote Sensing for the Environment (MicroRad). [Link]
  • Igor Yanovsky, Jing Qin and Bjorn Lambrigtsen (2020): Spatio-Temporal Resolution Enhancement for Geostationary Microwave Data, 16th Specialist Meeting on Microwave Radiometry and Remote Sensing for the Environment (MicroRad). [Link]
  • Jing Qin, Harlin Lee, Jocelyn Chi, Lucas Drumetz, Jocelyn Chanussot, Yifei Lou, Andrea L. Bertozzi (2020): Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization, IEEE Transactions on Geoscience and Remote Sensing (to appear). [Link] [PDF] [Code]
  • Mujibur Chowdhury, Jing Qin, and Yifei Lou (2020): Non-blind and Blind Deconvolution under Poisson Noise using Fractional-order Total Variation, Journal of Mathematical Imaging and Vision, 62(9): 1238-1255. [Link] [Code]
  • Yuying Shi, Zhimei Huo, Jing Qin, and Yilin Li (2020): Automatic prior shape selection for image edge detection with modified Mumford-Shah model, Computers and Mathematics with Applications, 79(6): 1644-1660. [Link]
  • Mujibur Chowdhury, Jun Zhang, Jing Qin, and Yifei Lou (2020): Poisson Image Denoising Based on Fractional-Order Total Variation, Inverse Problems and Imaging, 14(1): 77-96. [Link] [Code]
  • Jing Qin, Harlin Lee, Jocelyn Chi, Jocelyn Chanussot, Yifei Lou and Andrea Bertozzi (2019): Fast Blind Hyperspectral Unmixing based on Graph Laplacian, The 10th Workshop on Hyperspectral Image and Signal Processing, pp.1-5. [PDF]
  • Rachel Grotheer, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2019): Stochastic Iterative Hard Thresholding for Low-Tucker-rank Tensor Recovery, Linear and Multilinear Algebra (to appear) [Preprint]
  • Rachel Grotheer, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2019): Iterative Hard Thresholding for Low CP-rank Tensor Models [Preprint] [Code]
  • Jing Qin and Yifei Lou (2019): L_{1-2} Regularized Logistic Regression, 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2019, pp. 779-783. [Link]
  • Xin Wang, Shuai Xu, Zhen Ye, Chaozheng Zhou, and Jing Qin (2019): Evolution Model Based on Prior Information for Narrow Joint Segmentation, Journal of the Operations Research Society of China, 7:629–642. [Link]
  • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2019): Jointly Sparse Signal Recovery with Prior Info, 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2019, pp. 645-649. [Link]
  • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2019): Compressed Anomaly Detection with Multiple Mixed Observations, Gasparovic E., Domeniconi C. (eds) Research in Data Science. Association for Women in Mathematics Series, vol 17, 211-237. Springer. [Link] [Code]
  • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2019): Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors, Gasparovic E., Domeniconi C. (eds) Research in Data Science. Association for Women in Mathematics Series, vol 17, 1-14 Springer. [Link]
  • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2019): Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity, EMBC'19, pp.4758-4761, Berlin, Germany. [Link]
  • Jing Qin, Yushan Wang, and Wentai Liu (2018): Current Design with Minimum Error in Transcranial Direct Current Stimulation, S. Wang et al. (Eds.): BI 2018, LNAI 11309, pp. 52–62. [Link]
  • Feng Liu, Shouyi Wang, Jing Qin, Yifei Lou, and Jay Rosenberger (2018): Estimating Latent Brain Sources with Low-Rank Representation and Graph Regularization, S. Wang et al. (Eds.): BI 2018, LNAI 11309, pp. 304–316. [Link] (Best Paper Award)
  • Jing Qin, and Igor Yanovsky (2018): Robust Super-Resolution Image Reconstruction Method For Geometrically Deformed Remote Sensing Images, 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS2018), pp.8054-8057, July, Valencia, Spain. [Link]
  • Feng Liu, Jay Rosenberger, Jing Qin, Yifei Lou, and Shouyi Wang (2018): Task-Related EEG Source Localization via Graph Regularized Low-Rank Representation Model, Technical Report. COSMOS 18-01, University of Texas at Arlington. [Link]
  • Jing Qin, Xiyu Yi, and Shimon Weiss (2018): A Novel Fluorescence Microscopy Image Deconvolution Approach, IEEE International Symposium on Biomedical Imaging (ISBI2018), pp. 441-444, Washington D.C., April. [Link]
  • Jing Qin, Feng Liu, Shouyi Wang, and Jay Rosenberger (2017): EEG Source Imaging Based on Spatial and Temporal Graph Structures, 2017 International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), Montreal, Canada, Nov. [Link]
  • Feng Liu, Jing Qin, Shouyi Wang, Jay Rosenberger, and Jianzhong Su (2017): Supervised EEG Source Imaging with Graph Regularization in Transformed Domain, In: Zeng Y. et al. (eds) Brain Informatics. BI 2017. Lecture Notes in Computer Science, vol 10654, pp.59-71. Springer, Cham. [Link]
  • Fang Li, Jing Qin (2017): A robust fuzzy local information and L_p-norm distance based image segmentation method, IET Image Processing, 11(4): 217-226. [Link]
  • Jing Qin, Tianyu Wu, Ying Li, Wotao Yin, Stanley Osher, and Wentai Liu (2017): Accelerated High-Resolution EEG Source Imaging, 8th International IEEE EMBS Conference on Neural Engineering (NER' 17), pp.1-4, Shanghai, China, May. [Link] [PDF]
  • Ying Li, Jing Qin, Stanley Osher, and Wentai Liu (2016): Graph Fractional-Order Total Variation EEG Source Reconstruction, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC' 16), pp. 101-104, Orlando, Florida. [Link] [PDF]
  • Ying Li, Jing Qin, Yue-Loong Hsin, Stanley Osher, and Wentai Liu (2016): s-SMOOTH: Sparsity and Smoothness Enhanced EEG Brain Tomography, Frontiers in Neuroscience, 10: 543. [Link]
  • Jing Qin, Igor Yanovsky, and Wotao Yin (2015): Efficient Simultaneous Image Deconvolution and Upsampling Algorithm for Low Resolution Microwave Sounder Data, J. Appl. Remote Sens. 9(1), 095035. [PDF]
  • Fang Li, Stanley Osher, Jing Qin, and Ming Yan (2015): A Multiphase Image Segmentation Based on Fuzzy Membership Functions and L1-norm Fidelity, J. Sci. Comp. 69(1): 82-106. [Link] [PDF]
  • Jing Qin, Thomas Laurent, Kevin Bui, Ricardo V. Tan, Jasmine Dahilig, Shuyi Wang, Jared Rohe, Justin Sunu, Andrea L. Bertozzi (2015): Detecting Plumes in LWIR Using Robust Nonnegative Matrix Factorization with Graph-based Initialization, 94720V-94720V-11, SPIE DSS 2015. [Link] [PDF]
  • Jing Xu, Hui-Bin Chang, and Jing Qin (2014): Domain Decomposition Method for Image Deblurring, Journal of Computational and Applied Mathematics 271: 401-414. [Link]
  • Weihong Guo, Jing Qin, and Sibel Tari (2014): Automatic prior shape selection for image segmentation, Research in Shape Modeling, Chapter 1, pp. 1-8. [PDF]
  • Jing Qin, Xiyu Yi, Shimon Weiss, and Stanley Osher (2014): Shearlet-TGV Based Fluorescence Microscopy Image Deconvolution, UCLA CAM Reports: 14-32. [PDF]
  • Yaxin Peng, Shihui Ying, Jing Qin, and Tieyong Zeng (2013): Trimmed strategy for affine registration of point sets, J. Appl. Remote Sens. 7(1): 073468/1-10. [Link]
  • Jing Qin, Weihong Guo (April, 2013): An Efficient Compressive Sensing MR Image Reconstruction Scheme, International Symposium on BIOMEDICAL IMAGING: From Nano to Macro 2013. [Link]
  • Weihong Guo, Jing Qin and Wotao Yin: A NEW DETAIL-PRESERVING REGULARIZATION SCHEME, SIAM J. Imaging Sci. 7-2 (2014), pp. 1309-1334. [Link] [Code]
  • Weihong Guo, Jing Qin (May, 2013): A GEOMETRY GUIDED IMAGE DENOISING SCHEME, Inverse Problems and Imaging 7(2): 499-521. [Link]
  • Jing Qin, Weihong Guo (April 2nd, 2011): AN AUTOMATIC ADDITIVE AND MULTIPLICATIVE NOISE REMOVAL SCHEME WITH SHARPNESS PRESERVATION, International Symposium on BIOMEDICAL IMAGING: From Nano to Macro 2011. (NIH Travel Award) [Link]
  • Yaxing Peng, Fang Li, Jing Qin, Chaomin Shen (2007): Speckle removal of multi-polarization SAR imagery using variational method, SPIE Fifth International Symposium on Multispectral Image Processing and Pattern Recognition. [Link]

Thesis

  • Jing Qin. Prior Information Guided Image Processing and Compressive Sensing. PhD Diss. Case Western Reserve University, 2013. [Link]
  • Jing Qin. Tensor Voting Algorithm and Its Application. (Master Thesis). China Master's Theses Full-text Database. Oct. 2008. [Link]

Patents

  • Ying Li, Jing Qin, and Wentai Liu, "Brain Imaging System Using Total Variation EEG Source Reconstruction Method", UC-2016-681.
  • Ying Li, Wentai Liu, Jing Qin, Chih-Wei Chang, and Yi-Kai Lo, "Ultra-Dense Electrode-Based Brain Imaging System With High Spatial And Temporal Resolution", UC-2016-151-1.

Organized Conference/Workshop/Mini-symposium

  • Workshop on Recent Developments on Mathematical/Statistical Approaches in Data Science, Dallas, TX. (NSF awarded proposal) [Link]
  • Special session: Women in Data Science, 2019 AWM Research Symposium, Houston, TX. [Link]
  • 11th International Conference on Brain Informatics, Arlington, TX. [Link]
  • MS4: Graph Techniques for Image Processing, SIAM Conference on Imaging Sciences 2018, Bologna, Italy. [Link]
  • MS51, 61, 70: Nonconvex Regularization in Imaging: Theory, Algorithms and Applications, SIAM Conference on Imaging Sciences 2016, Albuquerque, NM. [Link]
  • Variational image analysis and applications, The 8th International Congress on Industrial and Applied Mathematics, 2015, Beijing, China.

Presentations

  • Jing Qin (May 10, 2019): Stochastic Greedy Algorithms for Multiple Measurement Vectors, International Conference of Union of Mathematical Imaging, Beijing, China.
  • Jing Qin (April 6-7, 2019): Graph Regularizations in EEG Source Localization, High-Resolution Flrorescence Microscopy Image Deconvolution, 2019 AWM Research Symposium, Houston, TX, USA.
  • Jing Qin (Dec 17-19, 2018): Stochastic Greedy Algorithms for Multiple Measurement Vectors, The 4th International Conference on Big Data and Information Analytics, Houston, TX, USA.
  • Jing Qin (Sep 28, 2018): Stochastic Greedy Algorithms for Multiple Measurement Vectors, Mathematics Department Colloquium, New Mexico State University, Las Cruces, NM, USA.
  • Jing Qin (June 5-8, 2018): EEG Source Imaging based on Spatial and Temporal Graph Structures, High-Resolution Fluorescence Microscopy Image Deconvolution, SIAM Conference on Imaging Science, Bologna, Italy.
  • Jing Qin (Oct 17, 2017): Fast high-resolution EEG source imaging, Annual Data Institute Conference 2017, San Francisco, CA.
  • Jing Qin (June 22-23, 2017): Graph Fractional-Order Total Variation EEG Source Reconstruction, Colloquium of Applied Mathematics, East China Normal University/Shanghai University, Shanghai, China.
  • Jing Qin (May 25, 2016): Smoothness and Sparsity Enhanced EEG Image Reconstruction, SIAM Conference on Imaging Sciences, Albuquerque, NM.
  • Jing Qin (August 15-16, 2015): Smoothness and Sparsity Enhanced Image Processing and Reconstruction, International Workshop on Mathematical Image Processing, Tianjin, China.
  • Jing Qin (August 10-14, 2015): Fuzzy Image Segmentation Based on TV Regularization and L1-norm Fidelity, ICIAM 2015, Beijing, China
  • Jing Qin (July 12th, 2015): Detecting Plumes in LWIR Using Robust Nonnegative Matrix Factorization with Graph-based Initialization, NSF DTRA workshop, Washington D.C.
  • Jing Qin (April 22nd, 2015): Detecting Plumes in LWIR Using Robust Nonnegative Matrix Factorization with Graph-based Initialization, SPIE 2015 DSS, Baltimore, MD.
  • Jing Qin (April 11th, 2015): AWM Minisymposium 2015
  • Jing Qin, Weihong Guo (Jan 18th, 2014): Prior Information Guided Image Denoising and Reconstruction, AWM Workshop 2014
  • Jing Qin, Weihong Guo (May 20th, 2012): Robust High Frequency Information Guided Compressive Sensing Reconstruction, SIAM Conference on Imaging Science 2012(IS12) CP1. (Student Travel Award)
  • Jing Qin, Weihong Guo (May 11th, 2012): VISUALIZATION IN MATHEMATICAL IMAGE DENOISING AND COMPRESSED SENSING RECONSTRUCTION (poster), Data Visualization Symposium 2012, CWRU.
  • Jing Qin, Weihong Guo (August 12, 2011): An Automatic Additive and Multiplicative Noise Removal Scheme with Sharpness Preservation (poster), Mathematical Methods for Images and Surfaces Conference, 2011, MSU.
  • Jing Qin, Weihong Guo (April 13th, 2010): A Segmentation Boosted Denoising Scheme for Images with Excessive and Inhomogeneous Noise, SIAM Conference on Imaging Science 2010(IS10) CP3.