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

TL;DR

This paper introduces a CNN-based method for hyperspectral image reconstruction from CTIS data, achieving higher accuracy and faster processing than traditional algorithms, and capable of handling multiple image types for real-time applications.

Contribution

The paper presents a novel CNN approach that improves reconstruction speed and accuracy for hyperspectral images from CTIS data, handling multiple image types simultaneously.

Findings

Higher reconstruction precision than expectation maximization

Shorter processing times for large spectral channels

Effective handling of diverse real-world images

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 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.