# Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training

## TL;DR

This paper introduces an unsupervised GAN-based method for transforming thermal infrared images into realistic visible spectrum images, overcoming dataset alignment issues and outperforming supervised methods in realism and generalization.

## Contribution

The paper proposes a novel unsupervised GAN approach for TIR to visible spectrum translation that does not require aligned image pairs, improving realism and generalization.

## Key findings

- Produces more realistic and sharp VIS images than supervised methods
- Generalizes well to new environments and datasets
- Outperforms existing state-of-the-art supervised approaches

## Abstract

Thermal Infrared (TIR) cameras are gaining popularity in many computer vision applications due to their ability to operate under low-light conditions. Images produced by TIR cameras are usually difficult for humans to perceive visually, which limits their usability. Several methods in the literature were proposed to address this problem by transforming TIR images into realistic visible spectrum (VIS) images. However, existing TIR-VIS datasets suffer from imperfect alignment between TIR-VIS image pairs which degrades the performance of supervised methods. We tackle this problem by learning this transformation using an unsupervised Generative Adversarial Network (GAN) which trains on unpaired TIR and VIS images. When trained and evaluated on KAIST-MS dataset, our proposed methods was shown to produce significantly more realistic and sharp VIS images than the existing state-of-the-art supervised methods. In addition, our proposed method was shown to generalize very well when evaluated on a new dataset of new environments.