Longwave infrared multispectral image sensor system using aluminum-germanium plasmonic filter arrays

TL;DR

This paper presents a novel LWIR multispectral image sensor system using aluminum-germanium plasmonic filters, enabling enhanced spectral imaging in the longwave infrared range for applications like thermography and object detection.

Contribution

The work introduces a new LWIR multispectral sensor with three wavelength bands using aluminum-germanium plasmonic filters integrated with a thermal sensor, demonstrating improved spectral imaging capabilities.

Findings

Successful fabrication of LWIR multispectral filter arrays

Enhanced thermal imaging resolution through deep learning reconstruction

Potential applications in thermography and spectral analysis

Abstract

A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and colour filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR: 8 to 14 microns) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum-based plasmonic filter array sandwiched in germanium. To realize the multispectral sensor, the filter arrays are then integrated into a 3D printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-the-art deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.