The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images

• URL: http://arxiv.org/abs/2108.13458v1
• Date: Mon, 30 Aug 2021 18:11:08 GMT
• Title: The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images
• Authors: Wei-Chih Huang, Mads Svanborg Peters, Mads Juul Ahlebaek, Mads Toudal Frandsen, Ren\'e Lynge Eriksen, and Bjarke J{\o}rgensen
• Abstract summary: A novel method, utilizing convolutional neural networks (CNNs), is proposed to reconstruct hyperspectral cubes from computed imaging spectrometer (CTIS) images. CNNs deliver higher precision and shorter reconstruction time than a standard expectation algorithm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A novel method, utilizing convolutional neural networks (CNNs), is proposed to reconstruct hyperspectral cubes from computed tomography imaging spectrometer (CTIS) images. Current reconstruction algorithms are usually subject to long reconstruction times and mediocre precision in cases of a large number of spectral channels. The constructed CNNs deliver higher precision and shorter reconstruction time than a standard expectation maximization algorithm. In addition, the network can handle two different types of real-world images at the same time -- specifically ColorChecker and carrot spectral images are considered. This work paves the way toward real-time reconstruction of hyperspectral cubes from CTIS images.

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