The application of convolutional neural networks for tomographic reconstruction of hyperspectral images

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 sparse expectation maximization algorithm. In addition, the network can handle two different types of real-world images at the same fime-specifically ColorChecker and carrot spectral images are considered. This work paves the way toward real-time reconstruction of hyperspectral cubes from CTIS images.

Automated summary

In this study, a novel method utilizing CNNs is proposed for reconstructing hyperspectral cubes from CTIS images. The constructed CNNs demonstrate higher precision and shorter reconstruction time compared to existing algorithms. Furthermore, the network can handle different types of real-world images simultaneously.