Name
Abstract | ||
|---|---|---|
We present an adaptive imaging technique that optically computes a low-rank approximation of a scene’s hyperspectral image, conceptualized as a matrix. Central to the proposed technique is the optical implementation of two measurement operators: a spectrally coded imager and a spatially coded spectrometer. By iterating between the two operators, we show that the top singular vectors and singular values of a hyperspectral image can be adaptively and optically computed with only a few iterations. We present an optical design that uses pupil plane coding for implementing the two operations and show several compelling results using a lab prototype to demonstrate the effectiveness of the proposed hyperspectral imager.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3345553 | ACM Transactions on Graphics |
Keywords | Field | DocType |
Krylov subspaces,coded apertures,optical computing | Krylov subspace,Singular value,Inference,Computer science,Algorithm,Hyperspectral imaging,Coding (social sciences),Operator (computer programming),Optical computing | Journal |
Volume | Issue | ISSN |
38 | 5 | 0730-0301 |
Citations | PageRank | References |
5 | 0.43 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vishwanath Saragadam | 1 | 7 | 3.49 |
| Aswin C. Sankaranarayanan | 2 | 770 | 51.51 |