High-Fidelity Infrared Scenario Simulation with Conditional CycleGAN

Infrared (IR) scenario generators are essential for development and validation of IR-based imaging and surveillance systems using synthetic signals in place of costly and scarce real-world acquisitions. Traditional physics-based simulation models struggle to reproduce realistic terrain textures and atmospheric artifacts such as clouds. To address this limitation, we propose a Conditional CycleGAN model for enhancing simulated IR images, allowing for the guided generation of specific scene features. Our approach translates low-fidelity simulated data into high-fidelity IR images while enabling user control over scene attributes. Experimental evaluations demonstrate that our method produces visually and statistically accurate textures, improving the realism of synthetic IR data.