• 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), July, Valencia, Spain. (accepted)
  • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2018): Compressed Anomaly Detection with Multiple Mixed Observations. arXiv:1801.10264(accepted)
  • 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.
  • 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. DOI: 10.1109/ISBI.2018.8363611
  • Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, and Jing Qin (2017): Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors. arXiv:1711.02743(accepted)
  • Jing Qin, Shuang Li, Deanna Needell, Anna Ma, Rachel Grotheer, Chenxi Huang, and Natalie Durgin (2017): Stochastic Greedy Algorithms for Multiple Measurement Vectors.  arXiv:1711.01521
  • 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. DOI: 10.1109/IPTA.2017.8310089
  • 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. DOI: 10.1007/978-3-319-70772-3_6
  • Fang Li, Jing Qin (2017): A robust fuzzy local information and L_p-norm distance based image segmentation method, IET Image Processing, DOI: 10.1049/iet-ipr.2016.0539
  • 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. DOI: 10.1109/NER.2017.8008277, UCLA CAM16-75
  • 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. Link1, Link2
  • 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. Link
  • 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. Link1, Link2
  • 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 SPIE DSS 2015. Link1, Link2
  • 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. Link
  • Jing Qin, Xiyu Yi, Shimon Weiss, and Stanley Osher (2014): Shearlet-TGV Based Fluorescence Microscopy Image Deconvolution. UCLA CAM Reports: 14-32. Link
  • 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
  • Jing Qin, Weihong Guo (2013): Two-stage Geometric Information Guided Compressive Imaging, Link
  • 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


  • 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


  • 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.


  • Nonconvex Regularization in Imaging: Theory, Algorithms and Applications, SIAM Conference on Imaging Sciences 2016, Albuquerque, NM.
  • Variational image analysis and applications, The 8th International Congress on Industrial and Applied Mathematics, 2015, Beijing, China.


  • 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.

Softwares and demos