# Computational Imaging Method for Thermal Infrared Hyperspectral Imaging Based on a Snapshot Divided-Aperture System

## TL;DR

A new camera system captures thermal and multispectral data in one shot, and a neural network reconstructs detailed hyperspectral data from it.

## Contribution

A novel snapshot divided-aperture system and neural network method for reconstructing high-resolution thermal infrared hyperspectral data from low-dimensional measurements.

## Key findings

- A divided-aperture camera captures thermal and multispectral data in a single exposure.
- A neural network successfully reconstructs 127-channel hyperspectral data from 9-channel measurements.
- Precise image registration is achieved using a star-point array calibration method.

## Abstract

What are the main findings?
A divided-aperture snapshot thermal infrared multispectral camera is developed, enabling single-exposure acquisition of both thermal images and multispectral data, with precise sub-channel image registration achieved via a star-point array calibration method.A neural network-based computational imaging method is proposed, successfully reconstructing 127-channel hyperspectral data from only 9-channel low-dimensional multispectral measurements.

A divided-aperture snapshot thermal infrared multispectral camera is developed, enabling single-exposure acquisition of both thermal images and multispectral data, with precise sub-channel image registration achieved via a star-point array calibration method.

A neural network-based computational imaging method is proposed, successfully reconstructing 127-channel hyperspectral data from only 9-channel low-dimensional multispectral measurements.

What is the implication of the main findings?
This method achieves reconstruction from a multispectral to hyperspectral data cube while preserving system compactness and snapshot capability, offering a potential tool for hyperspectral sensing in fields such as environmental monitoring and industrial inspection.

This method achieves reconstruction from a multispectral to hyperspectral data cube while preserving system compactness and snapshot capability, offering a potential tool for hyperspectral sensing in fields such as environmental monitoring and industrial inspection.

To address the technical challenge of simultaneously achieving snapshot imaging capability and high spectral resolution in thermal infrared spectral imaging, this paper proposes a computational imaging method based on a snapshot divided-aperture imaging system. In this method, a self-developed divided-aperture snapshot multispectral camera is utilized to simultaneously capture nine low-spectral-resolution images in a single exposure. The precise registration of the sub-channel images is accomplished via a star-point array calibration method. To construct the spectral reconstruction dataset, a Fourier-transform infrared hyperspectral camera (FTIR HCam) is employed to simultaneously acquire hyperspectral data from real-world scenes. Based on this, a neural network model is applied to reconstruct 127-channel hyperspectral information from the low-dimensional multispectral measurements. Experimental results demonstrate that the proposed method effectively achieves hyperspectral reconstruction while maintaining system compactness and snapshot imaging capability, thus providing a viable technical approach for hyperspectral sensing in dynamic thermal infrared scenarios.