Implicit Multi-Spectral Transformer: An Lightweight and Effective
  Visible to Infrared Image Translation Model
Yijia Chen Pinghua Chen Xiangxin Zhou Yingtie Lei Ziyang Zhou Mingxian Li
In the field of computer vision, visible light images often exhibit low
contrast in low-light conditions, presenting a significant challenge. While
infrared imagery provides a potential solution, its utilization entails high
costs and practical limitations. Recent advancements in deep learning,
particularly the deployment of Generative Adversarial Networks (GANs), have
facilitated the transformation of visible light images to infrared images.
However, these methods often experience unstable training phases and may
produce suboptimal outputs. To address these issues, we propose a novel
end-to-end Transformer-based model that efficiently converts visible light
images into high-fidelity infrared images. Initially, the Texture Mapping
Module and Color Perception Adapter collaborate to extract texture and color
features from the visible light image. The Dynamic Fusion Aggregation Module
subsequently integrates these features. Finally, the transformation into an
infrared image is refined through the synergistic action of the Color
Perception Adapter and the Enhanced Perception Attention mechanism.
Comprehensive benchmarking experiments confirm that our model outperforms
existing methods, producing infrared images of markedly superior quality, both
qualitatively and quantitatively. Furthermore, the proposed model enables more
effective downstream applications for infrared images than other methods.