Welcome to CIG!

Computational Imaging is a rapidly growing research area at the intersection of data science, computer vision, machine learning, applied mathematics, and physical sciences. Computational Imaging Group (CIG) develops advanced algorithms for fast and reliable acquisition, efficient processing, and automated analysis of imaging data, including 2D, 3D, or 4D images and videos. Applications include biomedical imaging, material sciences, geospatial sensing, space exploration, industrial inspection, and consumer electronics. We develop algorithms and theoretical foundations for enabling advanced capabilities in future imaging and sensing systems. We have expertise in deep learning, large-scale optimization, signal and image processing, computer vision, and statistical inference. Our research is inherently interdisciplinary and includes collaborations with researchers in optics, medicine, physics, materials, and biology.

Interested in doing research in CIG? Multiple opportunities at all levels.


Latest News

May 2024: Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis was accepted to ICML 2024.

April 2024: SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction was accepted to Magnetic Resonance in Medicine.

April 2024: Ulugbek gave an invited talk on Computational Imaging: Restoration Deep Networks as Image Priors at the workshop on Imaging Inverse Problems and Generative Models in Edinburgh, UK.

March 2024: PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction was accepted to IEEE Open Journal of Signal Processing.

March 2024: Domain Expansion via Network Adaptation for Solving Inverse Problems was accepted to IEEE Transactions on Computational Imaging.

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