FIRe-GAN: a novel deep learning-based infrared-visible fusion method for wildfire imagery

Wildfire detection is of paramount importance to avoid as much damage as possible to the environment, properties, and lives. In this regard, the fusion of thermal and visible information into a single image can potentially increase the robustness and accuracy of wildfire detection models. In the field of visible-infrared image fusion, there is a growing interest in Deep Learning (DL)-based image fusion techniques due to their reduced complexity; however, the 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. In the present work, we select three state-of-the-art (SOTA) DL-based image fusion techniques and evaluate them for the specific task of fire image fusion, and 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 and fused images.