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.
- Pub Date:
- January 2018
- Electrical Engineering and Systems Science - Image and Video Processing;
- Computer Science - Artificial Intelligence;
- Computer Science - Computer Vision and Pattern Recognition
- 14 pages of main paper and 15 pages of supplementary material