KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images

TL;DR

This paper introduces an optical method for efficiently capturing hyperspectral images by adaptively computing low-rank approximations using a novel optical setup with pupil plane coding, demonstrated through lab experiments.

Contribution

It presents a new optical imaging technique that adaptively computes low-rank hyperspectral representations using a novel pupil plane coding approach.

Findings

Effective optical computation of hyperspectral data achieved

Lab prototype demonstrates practical viability

Few iterations needed for accurate low-rank approximation

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.