# AI-Based Enhancing of xBn MWIR Thermal Camera Performance at 180 Kelvin

## TL;DR

This paper shows how AI can improve thermal camera performance at higher temperatures, leading to smaller and cheaper devices.

## Contribution

The novel use of AI to enable xBn MWIR cameras to operate effectively at 180 Kelvin without significant noise.

## Key findings

- AI algorithms successfully reduce thermal noise in xBn MWIR cameras at 180 Kelvin.
- Daylight performance of 180 Kelvin cameras is viable for remote sensing applications.
- Enhanced image quality at higher temperatures suggests potential for compact and affordable thermal systems.

## Abstract

Thermal imaging technology has revolutionized various fields, but current high operating temperature (HOT) mid-wave infrared (MWIR) cameras, particularly those based on xBn detectors, face limitations in size and cost due to the need for cooling to 150 Kelvin. This study explores the potential of extending the operating temperature of these cameras to 180 Kelvin, leveraging advanced AI algorithms to mitigate the increased thermal noise expected at higher temperatures. This research investigates the feasibility and effectiveness of this approach for remote sensing applications, combining experimental data with cutting-edge image enhancement techniques like Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). The findings demonstrate the potential of 180 Kelvin operation for xBn MWIR cameras, particularly in daylight conditions, paving the way for a new generation of more affordable and compact thermal imaging systems.