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.

To WashU students: If you want to explore research opportunities at CIG go to the list of available projects.


Latest News

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: Ulugbek spoke at the ECE Colloquium Series at University of Minnesota. The title of the talk was “Computational Imaging: Reconciling Models and Learning.”

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.

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