The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images
This work addresses the need for real-time, high-precision hyperspectral image reconstruction in applications like remote sensing or medical imaging, though it appears incremental as it applies an existing neural network approach to a specific domain.
The authors tackled the problem of slow and imprecise reconstruction of hyperspectral cubes from CTIS images by proposing a CNN-based method, which achieved higher precision and shorter reconstruction times compared to a sparse expectation maximization algorithm, as demonstrated on ColorChecker and carrot spectral 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 time -- specifically ColorChecker and carrot spectral images are considered. This work paves the way toward real-time reconstruction of hyperspectral cubes from CTIS images.