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.
High-Fidelity Infrared Scenario Simulation with Conditional CycleGAN
Dell'Acqua F.;
2026-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
