February 2020: New paper Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling.
February 2020: Our paper A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network was accepted to IEEE Signal Processing Letters.
February 2020: Our work “Image reconstruction for MRI using deep CNN priors trained without ground truth” was accepted to ISBI 2020 Workshop on Deep Learning for Biomedical Image Reconstruction taking place in 3-7 April 2020 in Iowa City, IA, USA.
January 2020: Ulugbek delivers an invited talk at IPAM Workshop on Deep Learning and Medical Applications. The video recording of the talk is available here.
January 2020: Two submissions accepted to ISMRM 2020 taking place in 18-23 April 2020 in Sydney, Australia.
December 2019: New preprint RARE: Image Reconstruction using Deep Priors Learned without Ground Truth.
December 2019: New preprint SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors.
November 2019: Ulugbek delivers an invited talk on Computational Imaging: Reconciling Models and Learning at the Learning for Computational Imaging workshop of ICCV 2019 in Seoul, South Korea.
October 2019: Ulugbek delivers an invited talk on Computational Imaging: Reconciling Models and Learning at the Center for Molecular Spectroscopy and Dynamics of Korea University in Seoul, South Korea.
October 2019: Proximal Newton Methods for x-ray imaging with non-smooth regularization was accepted to the Computational Imaging Conference at IS&T Electronic Imaging 2020.
September 2019: Infusing Learned Priors into Model-Based Multispectral Imaging was accepted to IEEE CAMSAP 2019.
September 2019: Online Regularization by Denoising with Applications to Phase Retrieval will be presented at ICCV 2019 workshop on Learning for Computational Imaging.
September 2019: Plug-and-Play Priors for Reconstruction-based Placental Image Registration was accepted to MICCAI 2019 workshop on Perinatal, Preterm and Paediatric Image analysis (PIPPI).
August 2019: Ulugbek will be speaking at the Los Alamos National Laboratory (LANL) on 15 Aug 2019 in Los Alamos, NM, USA.
August 2019: Ulugbek will be giving a seminar on “Reconciling Model-Based and Data-Adaptive Imaging” at AI Summer Institute of the Oak Ridge National Laboratory (ORNL) on 6 Aug 2019 in Oak Ridge, TN, USA.
July 2019: New preprint A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network.
June 2019: Ulugbek will be speaking at IPAM Deep Learning and Medical Applications workshop taking place on 27-31 Jan 2020 in Los Angeles, CA, USA.
June 2019: Ulugbek is co-organizing a special session Recent Progress in Computational Microscopy at Electronic Imaging 2020. The session will take place on 27-28 Jan 2020 in Burlingame, CA USA.
May 2019: Holographic particle localization under multiple scattering was accepted to SPIE Advanced Photonics.
May 2019: New preprint Block Coordinate Regularization by Denoising.
May 2019: Xiaojian got an offer to join MERL this summer as an intern. Congratulations!
April 2019: Ulugbek served as an area chair for “Computational Imaging” at IEEE ICASSP 2019.
March 2019: Ulugbek is co-organizing an IMA Special Workshop on Computational Imaging. The workshop will take place on 14-18 Oct 2019 in Minneapolis, MN USA.
January 2019: An Online Plug-and-Play Algorithm for Regularized Image Reconstruction was accepted to IEEE Transactions on Computational Imaging.
January 2019: This semester Ulugbek will be teaching Optimization. Xiaojian will be the lead assistant instructor.
November 2018: Ulugbek was re-elected as a member of the IEEE Technical Committee on Computational Imaging for 2019-2021.
November 2018: Plenary by Ulugbek on Signal Processing for Nonlinear Diffractive Imaging and a talk by Yu on Stability of Scattering Decoder for Nonlinear Diffractive Imaging, both at iTWIST 2018.
November 2018: Doctoral course at iTWIST 2018 on Computational Imaging with Convex and Non-Convex Optimization.
November 2018: New preprint Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm.
October 2018: New preprint Image Restoration using Total Variation Regularized Deep Image Prior.
October 2018: Jiaming Liu is a recipient of the Dean’s Select PhD Fellowship from the Washington University, School of Engineering & Applied Science. Congratulations for this achievement!
October 2018: Ulugbek is serving as a program chair for OSA Mathematics in Imaging 2019 (24-27 Jun 2019 in Munich, Germany). Consider submitting to the conference. For more info see the conference flyer here.
September 2018: Our manuscript Sparse Blind Deconvolution for Distributed Radar Autofocus Imaging was accepted to IEEE Transactions on Computational Imaging.
September 2018: New preprint An Online Plug-and-Play Algorithm for Regularized Image Reconstruction.
August 2018: Our manuscript Stability of Scattering Decoder For Nonlinear Diffractive Imaging was accepted to iTWIST 2018.
August 2018: New preprint Single-shot holographic 3D particle-localization under multiple scattering.
July 2018: New preprint signProx: One-Bit Proximal Algorithm for Nonconvex Stochastic Optimization.
July 2018: Building a better microscope describes our NSF-sponsored project on high-resolution microscopy.
July 2018: New preprint Stability of Scattering Decoder For Nonlinear Diffractive Imaging.
July 2018: The list of research projects at CIG available to the current WashU students is here.
June 2018: The source code for Efficient and accurate inversion of multiple scattering with deep learning is now available for download.
June 2018: Our project on data-adaptive imaging was awarded WUSTL Collaboration Initiation Grant.
May 2018: Our manuscript Efficient and accurate inversion of multiple scattering with deep learning was accepted to OSA Optics Express.
May 2018: New arXiv manuscript Sparse Blind Deconvolution for Distributed Radar Autofocus Imaging.
May 2018: Ulugbek will chair the session “Sparsity Based Priors” at OSA Imaging and Applied Optics Congress 2018 in Orlando, FL, USA.
May 2017: Our manuscript Learning-based Image Reconstruction via Parallel Proximal Algorithm was accepted to IEEE Signal Processing Letters.
May 2018: The patent application System and Method for Through-the-Wall-Radar-Imaging using Total-Variation Denoising was granted by USPTO.
April 2018: Ulugbek is a recipient of the IEEE Signal Processing Society’s 2017 Best Paper Award (with V. K. Goyal and S. Rangan). The award was delivered during IEEE ICASSP 2018 in Calgary, Canada.
April 2018: Sergio Goodwin, Jhoan Hernandez, and Jason Liao will join CIG as summer undergraduate researchers. Sergio and Jhoan join via the Washington University Summer Engineering Fellowship (WUSEF) program.
April 2018: Ulugbek chaired the session “Computational Imaging II” at IEEE ICASSP 2018 in Calgary, Canada.
March 2018: New arXiv manuscript Efficient and accurate inversion of multiple scattering with deep learning.
March 2018: The patent application Method and System for Through-the-Wall Radar Imaging was granted by USPTO.
March 2018: Ulugbek talks at the Interdisciplinary Distinguished Seminar Series of NC State University, Raleigh, NC, USA.
February 2018: Xiaojian Xu joins CIG as a PhD student. Welcome!
January 2018: New arXiv manuscript Learning-based Image Reconstruction via Parallel Proximal Algorithm.
January 2018: Wenmei Bo joins CIG as a MSc student starting from January 2018. Welcome!
January 2018: Terry Pan joins CIG as a MSc student starting from January 2018. Welcome!
December 2017: Shiqi Xu joins CIG as a MSc student starting from January 2018. Welcome!
November 2017: Yu Sun joins CIG as a PhD student starting from January 2018. Welcome!
October 2017: Our manuscript SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering was accepted to IEEE Transactions on Computational Imaging.
October 2017: Our manuscript A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems was accepted to IEEE Signal Processing Letters.
October 2017: Ulugbek serves as a program committee member for OSA Mathematics in Imaging conference.
October 2017: Ulugbek joined the Journal of Electronic Imaging (JEI) as an Associate Editor.
September 2017: Computational Imaging Group is now recruiting Ph.D. students [see more].
September 2017: Ulugbek joins Washington University in St. Louis as an Assistant Professor.
August 2017: Ulugbek delivers a plenary talk at the Workshop on Regularized Inverse Problem Solving and High-Dimensional Learning Methods in UC Louvain, Belgium.
June 2017: Ulugbek delivers an invited talk at OSA Mathematics in Imaging in San Francisco, CA, USA.
June 2017: Our accepted ICIP 2017 manuscript is now online “Online Convolutional Dictionary Learning for Multimodal Imaging.”
May 2017: New preprint “SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scattering.”
May 2017: Publication accepted to OSA Advanced Photonics Congress 2017: “Acceleration of FDTD-based Inverse Design Using a Neural Network Approach.”
May 2017: Two publications accepted to ICIP 2017: “Online Convolutional Dictionary Learning for Multimodal Imaging” and “Fusion of multi-angular aerial images based on epipolar geometry and matrix completion.”
April 2017: Ulugbek delivers a guest lecture for the class “Computational Optical Imaging” at Boston University on nonlinear algorithms for optical imaging.
March 2017: Two publications were accepted to SPARS 2017: “Learning Convolutional Proximal Filters” (with D. Liu and H. Mansour) and “A Kaczmarz Method for Low Rank Matrix Recovery” (with H. Mansour and O. Yilmaz).
February 2017: The manuscript “Compressive Imaging with Iterative Forward Models” is the Best Student Paper Award finalist at IEEE ICASSP 2016.
January 2017: The manuscript “Motion-Adaptive Depth Superresolution” was accepted to IEEE Transactions on Image Processing.
December 2016: Three publications and a letter were accepted to IEEE ICASSP 2016:
- “Compressive Imaging with Iterative Forward Models,” with H.-Y. Liu, D. Liu, H. Mansour, and P. T. Boufounos.
- “Optical Tomography based on a Nonlinear Model that Handles Multiple Scattering,” with M. H. Shoreh, A. Goy, J. Lim, M. Unser, and D. Psaltis.
- “Learning Optimal Nonlinearities for Iterative Thresholding Algorithms,” with H. Mansour.
November 2016: The manuscript “A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization” was accepted to IEEE Transactions on Image Processing.
November 2016: “Learning Bayesian Optimal FISTA with Error Backpropagation” was accepted for an invited session at International BASP Frontiers workshop 2017.
October 2016: The PhD thesis “Sparsity-Driven Statistical Inference for Inverse Problems” received a special distinction from EPFL’s award committee. The official press release is here.
October 2016: Serving as an area chair of ICASSP 2017 for computational imaging.
September 2016: The manuscript “Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization” was accepted to IEEE Transactions on Information Theory.
September 2016: Ulugbek delivers two keynote talks at CoSeRa 2016: “Coherent Distributed Array Imaging under Unknown Position Perturbations” and “Online Blind Deconvolution for Sequential Through-the-Wall-Radar-Imaging.”
August 2016: Ulugbek’s special session on “Large-Scale Computational Imaging with Wave Models” was accepted for ICASSP 2017. The session is organized jointly with Prof. Laura Waller and Dr. Brendt Wohlberg.
August 2016: Ulugbek delivers two keynote talks at iTWIST 2016: “Learning MMSE Optimal Thresholds for FISTA” and “Minimizing Isotropic Total Variation without Subiterations.”
July 2016: Three publications were accepted to CoSeRa 2016.
July 2016: Ulugbek delivers an invited talk at NYU Tandon School of Engineering on “Trainable iterative algorithms for computational sensing.”
June 2016: The manuscript “A Recursive Born Approach to Nonlinear Inverse Scattering” was accepted to IEEE Signal Processing Letters.
April 2016: The manuscript “Depth Superresolution using Motion Adaptive Regularization” was accepted to IEEE ICMEW 2016.
April 2016: Ulugbek delivers an invited talk at Tufts University‘s ECE Colloquia on “Compressive Imaging with Learning Tomography.”