CAREER: Provable, Flexible, & Scalable Integration of Physical and Diffusion Models for Pr — NSF Award to Johns Hopkins University
Computational imaging aims to recover meaningful visual information about an object or scene from measurements collected by an imaging system. In many important applications, however, those measurements are indirect, incomplete, and noisy, making it difficult to determine the true underlying image. For example, many th
| Award title | CAREER: Provable, Flexible, & Scalable Integration of Physical and Diffusion Models for Pr |
|---|---|
| Award ID | 2542022 |
| Awardee | Johns Hopkins University |
| City | BALTIMORE |
| State | MD |
| Amount obligated | $441,528 |
| Principal investigator | Yu Sun |
| Program | Comm & Information Foundations |
| Start date | 04/15/2026 |
| Abstract | Computational imaging aims to recover meaningful visual information about an object or scene from measurements collected by an imaging system. In many important applications, however, those measurements are indirect, incomplete, and noisy, making it difficult to determine the true underlying image. For example, many three-dimensional microscopy applications would need to recover cellular structure from measurements acquired over only limited views. In such settings, the data may be consistent wi |
| Source | NSF Awards |
$799/mo
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