Authors : Kabir Kohli

Volume/Issue : Volume 10 - 2025, Issue 8 - August

Google Scholar : https://tinyurl.com/2ac3zsyy

Scribd : https://tinyurl.com/whj42p6e

DOI : https://doi.org/10.38124/ijisrt/25aug556

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Abstract : Minimum Resolvable Temperature Difference (MRTD) remains a key indicator of thermal imaging system performance, reflecting the ability to distinguish subtle temperature variations at defined spatial frequencies. As applications expand into high-demand areas such as autonomous surveillance, military missions, and space exploration, achieving lower MRTD values becomes increasingly critical. Recent advancements highlight the transformative role of quantum detectors, like HgCdTe and Quantum Well Infrared Photodetectors (QWIPs), which offer improved sensitivity, reduced noise, and broader spectral response, significantly lowering MRTD thresholds. These technologies enhance thermal image resolution and clarity under challenging operational conditions. Concurrently, artificial intelligence (AI) is reshaping MRTD assessment by enabling real-time optimisation of imaging parameters. AI-driven algorithms adapt to environmental variables, scene complexity, and target features, facilitating automatic performance tuning and enhanced contrast. Machine learning techniques further support noise reduction and detail enhancement, pushing MRTD performance boundaries. Complementing these are adaptive resolution strategies that enable thermal systems to dynamically adjust spatial and thermal accuracy in response to operational demands. Additionally, innovations in sensor miniaturisation are fuelling the development of lightweight, portable thermal imagers for use in wearable and unmanned systems. These integrated technologies are defining a new era of high-performance, intelligent thermal imaging with unprecedented MRTD capabilities.

Keywords : MRTD, Thermal Clutter, Scene-Dependant MRTD Algorithm, AI-Assisted TI Calibration, Real-Time Environmental Compensation, Adaptive IR Imaging System

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