ERI: A New Uncertainty Modeling Framework for Snapshot Compressive Imaging — NSF Award to Rochester Institute of Tech (NY, $200,00
Computational imaging technologies are increasingly required to operate in real-world applications, offering high-fidelity visual output under complex lighting environments. Among these, snapshot compressive imaging (SCI) is a promising technique that retrieves high-dimensional signals from 2D optically compressed meas
| Award title | ERI: A New Uncertainty Modeling Framework for Snapshot Compressive Imaging |
|---|---|
| Award ID | 2502050 |
| Awardee | Rochester Institute of Tech |
| City | ROCHESTER |
| State | NY |
| Amount obligated | $200,000 |
| Principal investigator | Zhiqiang Tao |
| Program | ERI-Eng. Research Initiation |
| Start date | 10/01/2025 |
| Abstract | Computational imaging technologies are increasingly required to operate in real-world applications, offering high-fidelity visual output under complex lighting environments. Among these, snapshot compressive imaging (SCI) is a promising technique that retrieves high-dimensional signals from 2D optically compressed measurements. Incorporating modern AI techniques, SCI has significantly advanced the capabilities of traditional optical sensing in various fields, including hyperspectral imaging, vid |
| Source | NSF Awards |
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