Welcome to the Computational Imaging Group (CIG)!
Computational Imaging is a rapidly growing research area at the intersection of image processing, computer vision, physical sciences, and machine learning. Computational Imaging Group (CIG) at Washington University researches new methods for reliable acquisition, efficient processing, and automated analysis of spatiotemporal data. Application include biomedical imaging, material sciences, geospatial intelligence, space exploration, industrial inspection, and consumer electronics. We develop algorithms and theoretical foundations for enabling advanced capabilities in future imaging systems. We have expertise in large-scale optimization, signal and image processing, machine learning, computer vision, and statistical inference. Our research is inherently interdisciplinary and includes collaborations with researchers in optics, medicine, physics, and biology.
Interested in doing research with us? Multiple opportunities at all levels.
September 2021: Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition was accepted to NeurIPS 2021.
September 2021: New paper Deep-Learning-Based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient Recalled Echo (qGRE) MRI metrics associated with Human Brain Neuronal Structure and Hemodynamic Properties.
September 2021: New paper Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative R2* Mapping.
September 2021: Ulugbek is teaching ESE513/CSE534A Large-Scale Optimization for Data Science. There are 74 students taking the class.