KRISM—Krylov Subspace-based Optical Computing of Hyperspectral Images
KRISM—Krylov Subspace-based Optical Computing of Hyperspectral Images
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DOI：
10.1145/3345553
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发表时间：
2018-01
期刊：
影响因子：
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通讯作者：
Vishwanath Saragadam;Aswin C. Sankaranarayanan
中科院分区：
文献类型：
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作者：
Vishwanath Saragadam;Aswin C. Sankaranarayanan
我们提出了一种自适应成像技术，该技术可以光学计算场景高光谱图像的低秩近似，概念化为矩阵。该技术的核心是两个测量算子的光学实现：一个光谱编码成像仪和一个空间编码光谱仪。通过在两个算子之间的迭代，我们证明了高光谱图像的顶部奇异向量和奇异值可以自适应地光学计算，只需很少的迭代。我们提出了一种光学设计，使用瞳孔平面编码来实现这两种操作，并使用实验室原型展示了几个令人信服的结果，以证明所提出的高光谱成像仪的有效性。
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.