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Ruiming Cao on Muck Rack

Ruiming Cao

Berkeley
As seen in: Nature, BioRxiv
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Articles

Neural space–time model for dynamic multi-shot imaging - Nature Methods

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By Ruiming Cao, Nikita S. Divekar, James Nunez, James Nuñez, Srigokul Upadhyayula
| Nature Verified
Abstract Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space–time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction.
Open in Who Shared Issue with article?

Neural space-time model for dynamic scene recovery in multi-shot computational imaging systems

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By Nikita S. Divekar, James Nunez, James Nuñez, Srigokul Upadhyayula, Ruiming Cao
| BioRxiv
Abstract Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics. Hence, we can both remove motion artifacts and resolve sample dynamics.
Open in Who Shared Issue with article?
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