Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Model

TL;DR

This paper introduces an efficient Transformer-based model for converting visible light images into high-quality infrared images, addressing limitations of GAN-based methods with improved stability and output quality.

Contribution

The paper presents a novel end-to-end Transformer model with modules for feature extraction and fusion, outperforming existing methods in infrared image translation.

Findings

Model achieves superior image quality in benchmarks

Outperforms GAN-based methods in stability and output quality

Enhances downstream infrared image applications

Abstract

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.