Synthetic Infrared

Synthetic infrared (IR) image generation aims to create realistic IR images for training and testing computer vision algorithms, reducing the cost and risk of acquiring real-world data. Current research focuses on developing physics-informed deep learning models, such as diffusion models and generative adversarial networks (GANs), to accurately translate visible light images into IR images while adhering to the underlying physical laws of infrared radiation. This capability is crucial for advancing applications like small target detection, jet fire characterization, and drone-based object recognition, particularly in challenging conditions where real IR data is scarce or expensive to obtain. The availability of high-quality synthetic datasets is also driving improvements in algorithms for object detection and image analysis in the infrared spectrum.