The translation of thermal infrared (TIR) images to visible light (VI) images
presents a challenging task with potential applications spanning various
domains such as TIR-VI image registration and fusion. Leveraging supplementary
information derived from TIR image conversions can significantly enhance model
performance and generalization across these applications. However, prevailing
issues within this field include suboptimal image fidelity and limited model
scalability. In this paper, we introduce an algorithm, LadleNet, based on the
U-Net architecture. LadleNet employs a two-stage U-Net concatenation structure,
augmented with skip connections and refined feature aggregation techniques,
resulting in a substantial enhancement in model performance. Comprising
'Handle' and 'Bowl' modules, LadleNet's Handle module facilitates the
construction of an abstract semantic space, while the Bowl module decodes this
semantic space to yield mapped VI images. The Handle module exhibits
extensibility by allowing the substitution of its network architecture with
semantic segmentation networks, thereby establishing more abstract semantic
spaces to bolster model performance. Consequently, we propose LadleNet+, which
replaces LadleNet's Handle module with the pre-trained DeepLabv3+ network,
thereby endowing the model with enhanced semantic space construction
capabilities. The proposed method is evaluated and tested on the KAIST dataset,
accompanied by quantitative and qualitative analyses. Compared to existing
methodologies, our approach achieves state-of-the-art performance in terms of
image clarity and perceptual quality. The source code will be made available at
https://github.com/Ach-1914/LadleNet/tree/main/