# Thermal Infrared Colorization via Conditional Generative Adversarial Network

arXiv: https://arxiv.org/abs/1810.05399 · HTML: https://mlsanity.com/paper/1810.05399/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 15 of 15)

- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels](https://mlsanity.com/paper/2301.12689/) (2023)
- [Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention](https://mlsanity.com/paper/2104.14374/) (2021)
- [Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning](https://mlsanity.com/paper/2310.15688/) (2023)
- [Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary Labels](https://mlsanity.com/paper/2106.08091/) (2021)
- [Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization](https://mlsanity.com/paper/2208.02960/) (2022)
- [MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization](https://mlsanity.com/paper/2506.17540/) (2025)
- [Contrast-Guided Cross-Modal Distillation for Thermal Object Detection](https://mlsanity.com/paper/2511.01435/) (2025)
- [A Simple Mutual Information based Registration Method for Thermal-Optical Image Pairs applied on a Novel Dataset](https://mlsanity.com/paper/2101.06910/) (2021)
- [A Novel Deep Learning Method for Thermal to Annotated Thermal-Optical Fused Images](https://mlsanity.com/paper/2107.05942/) (2021)
- [Thermal infrared image based vehicle detection in low-level illumination conditions using multi-level GANs](https://mlsanity.com/paper/2209.09808/) (2022)
- [LatentColorization: Latent Diffusion-Based Speaker Video Colorization](https://mlsanity.com/paper/2405.05707/) (2024)
- [Convolutional Deep Colorization for Image Compression: A Color Grid Based Approach](https://mlsanity.com/paper/2502.05402/) (2025)
- [A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising](https://mlsanity.com/paper/2509.18801/) (2025)
- [FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization](https://mlsanity.com/paper/2605.15880/) (2026)

## Abstract (arXiv)

Transforming a thermal infrared image into a realistic RGB image is a challenging task. In this paper we propose a deep learning method to bridge this gap. We propose learning the transformation mapping using a coarse-to-fine generator that preserves the details. Since the standard mean squared loss cannot penalize the distance between colorized and ground truth images well, we propose a composite loss function that combines content, adversarial, perceptual and total variation losses. The content loss is used to recover global image information while the latter three losses are used to synthesize local realistic textures. Quantitative and qualitative experiments demonstrate that our approach significantly outperforms existing approaches.