KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
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Low-rank modeling of hyperspectral images has found extensive use in numerous inference tasks. In this paper, we present an adaptive imaging technique that optically computes a low-rank representation of the scene's hyperspectral image. We make significant contributions towards simultaneously highly resolvable spectral and spatial measurements by introducing pupil coding. The proposed imager, KRISM, provides optical implementation of two operators on the scene's hyperspectral image --- namely, a spectrally-coded spatial measurement and a spatially-coded spectral measurement. By iterating between the two operators, using the output of one as the input to the other, we show that the top singular vectors and singular values of a hyperspectral image can be computed in the optical domain with very few measurements. We present an optical setup and show several compelling real world examples that demonstrate the effectiveness of our proposed algorithm.
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