# FIRe-GAN: A novel Deep Learning-based infrared-visible fusion method for wildfire imagery

arXiv: https://arxiv.org/abs/2101.11745 · HTML: https://mlsanity.com/paper/2101.11745/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [MDLatLRR: A novel decomposition method for infrared and visible image fusion](https://mlsanity.com/paper/1811.02291/) (2018)
- [Infrared and Visible Image Fusion using a Deep Learning Framework](https://mlsanity.com/paper/1804.06992/) (2018)
- [When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method](https://mlsanity.com/paper/2009.01315/) (2020)

## Built on by (most cited first, 5 of 5)

- [Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2401.11582/) (2024)
- [An FPGA smart camera implementation of segmentation models for drone wildfire imagery](https://mlsanity.com/paper/2309.01318/) (2023)
- [Through-Foliage Surface-Temperature Reconstruction for Early Wildfire Detection](https://mlsanity.com/paper/2511.12572/) (2025)
- [Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach](https://mlsanity.com/paper/2206.02110/) (2022)
- [ForestProtector: An IoT Architecture Integrating Machine Vision and Deep Reinforcement Learning for Efficient Wildfire Monitoring](https://mlsanity.com/paper/2501.09926/) (2025)

## Abstract (arXiv)

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.