StawGAN: Structural-Aware Generative Adversarial Networks for Infrared
  Image Translation
Luigi Sigillo Eleonora Grassucci Danilo Comminiello
This paper addresses the problem of translating night-time thermal infrared
images, which are the most adopted image modalities to analyze night-time
scenes, to daytime color images (NTIT2DC), which provide better perceptions of
objects. We introduce a novel model that focuses on enhancing the quality of
the target generation without merely colorizing it. The proposed structural
aware (StawGAN) enables the translation of better-shaped and high-definition
objects in the target domain. We test our model on aerial images of the
DroneVeichle dataset containing RGB-IR paired images. The proposed approach
produces a more accurate translation with respect to other state-of-the-art
image translation models. The source code is available at
https://github.com/LuigiSigillo/StawGAN