  Early wildfire detection is of paramount importance to avoid as much damage
as possible to the environment, properties, and lives. Deep Learning (DL)
models that can leverage both visible and infrared information have the
potential to display state-of-the-art performance, with lower false-positive
rates than existing techniques. However, most DL-based image fusion methods
have not been evaluated in the domain of fire imagery. Additionally, to the
best of our knowledge, no publicly available dataset contains visible-infrared
fused fire images. There is a growing interest in DL-based image fusion
techniques due to their reduced complexity. Due to the latter, we select three
state-of-the-art, DL-based image fusion techniques and evaluate them for the
specific task of fire image fusion. We compare the performance of these methods
on selected metrics. Finally, we also present an extension to one of the said
methods, that we called FIRe-GAN, that improves the generation of artificial
infrared images and fused ones on selected metrics.
