KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
This addresses the challenge of high-dimensional data processing in hyperspectral imaging for applications like remote sensing or medical diagnostics, representing an incremental improvement in optical computing methods.
The paper tackles the problem of efficiently computing low-rank approximations of hyperspectral images by introducing an adaptive optical imaging technique that iteratively applies spectrally-coded and spatially-coded operators, achieving this with only a few iterations as demonstrated by lab prototype results.
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.