KRISM—Krylov Subspace-based Optical Computing of Hyperspectral Images
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 17, 2019
- Source ID
- 10.1145/3345553
Entities
People
- Aswin C. Sankaranarayanan
- Vishwanath Saragadam
Organizations
- Carnegie Mellon University
- Intel Corporation
- National Geospatial-Intelligence Agency
- National Science Foundation