CAREER: Reconciling Model-Based and Learning-Based Imaging: Theory, Algorithms, and Applic — NSF Award to University of Wisconsin-
Computational imaging is a rapidly growing area that seeks to enhance the capabilities of imaging instruments by viewing imaging as a computational problem. There are currently two distinct approaches for designing computational imaging methods: model-based and learning-based. Model-based methods leverage analytical si
| Award title | CAREER: Reconciling Model-Based and Learning-Based Imaging: Theory, Algorithms, and Applic |
|---|---|
| Award ID | 2625643 |
| Awardee | University of Wisconsin-Madison |
| City | MADISON |
| State | WI |
| Amount obligated | $54,998 |
| Principal investigator | Ulugbek Kamilov |
| Program | Comm & Information Foundations |
| Start date | 01/01/2026 |
| Abstract | Computational imaging is a rapidly growing area that seeks to enhance the capabilities of imaging instruments by viewing imaging as a computational problem. There are currently two distinct approaches for designing computational imaging methods: model-based and learning-based. Model-based methods leverage analytical signal properties and often come with theoretical guarantees and insights. Learning-based methods leverage data-driven representations for best empirical performance through training |
| Source | NSF Awards |
$799/mo
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