Exploring AI-Based Techniques for the Generation of Synthetic Infrared Images and Their Practical Applications

Infrared (IR) imaging plays a vital role in a wide range of applications, including military surveillance, autonomous driving, and medical diagnostics. However, the acquisition of high-quality IR data is often constrained by sensor cost, environmental limitations, and data availability. Recent advances in artificial intelligence, particularly in generative models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models, have enabled the synthesis of realistic IR images from alternative modalities like visible spectrum data. This paper presents a comprehensive survey of AI-based techniques for synthetic IR image generation, categorizing state-of-the-art approaches based on architecture, training strategy, and data modality. We identify current limitations in realism, generalizability, and explainability, while highlighting open research challenges. Building on the insights from this survey, we propose a novel deep learning framework designed to generate enhanced synthetic IR imagery using limited paired data. The proposed approach aims to improve domain adaptation performance and reduce computational overhead for real-time applications. Finally, we explore potential use cases in defense, healthcare, and intelligent systems, illustrating the transformative potential of AI-driven IR image synthesis.