Exploration and Validation of Specialized Loss Functions for Generative Visual-Thermal Image Domain Transfer

This paper presents an enhanced approach to visual-to-thermal image translation using an improved InfraGAN model, incorporating additional loss functions to increase realism and fidelity in generated thermal images. Building on the existing InfraGAN architecture, we introduce perceptual, style, and discrete Fourier transform (DFT) losses, aiming to capture intricate image details and enhance texture and frequency consistency. Our model is trained and evaluated on the FLIR Adas dataset, providing paired visual and thermal images across diverse contexts, from urban traffic scenes. To optimize the interplay of loss functions, we employ hyperparameter tuning with the Optuna library, achieving an optimal balance among the components of the loss function. First, experimental results show that these modifications lead to significant improvements in the quality of generated thermal images, underscoring the potential of advanced loss functions for domain transfer tasks. This work contributes a refined framework for generating high-quality thermal imagery, with implications for fields such as surveillance, autonomous driving, and facial recognition in challenging environmental conditions.