Welcome to the Computational Imaging Group!
The Computational Imaging Group (CIG) at Washington University in St. Louis pursues research on the development of advanced algorithms and mathematical tools for biomedical and industrial imaging.
Over the past two decades, there has been a significant paradigm shift in imaging research. Increasingly, researchers are coming to consensus that computation is a cornerstone of future imaging systems. Our research focuses on that aspect of imaging by developing computational methods that enable new imaging capabilities. Specifically, we aim to leverage the advances in optimization, machine learning, and statistical inference to design new models, algorithms, and systems for imaging. Our work is inherently interdisciplinary and has broad applications in biomedical imaging, defense, physical science, and industrial inspection. We extensively collaborate with researchers in optics, medicine, biology, as well as with those working in industry.
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.