# Accelerating computed tomographic imaging spectrometer reconstruction using a parallel algorithm exploiting spatial shift-invariance

arXiv: https://arxiv.org/abs/2006.01573 · HTML: https://mlsanity.com/paper/2006.01573/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [The Application of Convolutional Neural Networks for Tomographic Reconstruction of Hyperspectral Images](https://mlsanity.com/paper/2108.13458/) (2021)
- [The hybrid approach -- Convolutional Neural Networks and Expectation Maximization Algorithm -- for Tomographic Reconstruction of Hyperspectral Images](https://mlsanity.com/paper/2205.15772/) (2022)
- [Investigating the Applicability of a Snapshot Computed Tomography Imaging Spectrometer for the Prediction of Brix and pH of Grapes](https://mlsanity.com/paper/2411.03114/) (2024)

## Abstract (arXiv)

Computed Tomographic Imaging Spectrometers (CTIS) capture hyperspectral images in realtime. However, post processing the imagery can require enormous computational resources; thus, limiting its application to non-realtime scenarios. To overcome these challenges we developed a highly parallelizable algorithm that exploits spatial shift-invariance. To demonstrate the versatility of our new algorithm, we developed implementations on a desktop and an embedded graphics processing unit (GPU). To our knowledge, our results show the fastest image reconstruction times reported.