Optimizing Nighttime Infrared and Visible Image Fusion for Long-haul Tactile Internet

In the domain of infrared and visible image fusion, the majority of existing methods are designed for infrared and visible images with normal illumination conditions. However, these methods may not effectively address the challenges presented by long-haul transmission scenarios in the Tactile Internet. To meet the requirements of nighttime infrared and visible image fusion in long-haul network architectures for the tactile internet, an illumination component adjusting network (ICANet) is built. Firstly, an illumination adjustment denoising subnetwork (IADSubNet) is designed to enhance the illumination component of nighttime visible images and simultaneously eliminate noise. Secondly, a local-global perception fusion subnetwork (LGPFSubNet) is built to dynamically extract and fuse both global and local information of the source images. Furthermore, we leverage a mutual consistency loss to generate fused images that are both visually appealing and rich in information. This ensures the fidelity and consistency of the fused images during long-distance transmission. Comprehensive experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods quantitatively and qualitatively, and prove that it has potential for high performance in the long-haul transmission scenarios of tactile Internet. Meanwhile, the fused images generated by the ICANet significantly enhance object detection tasks. It is a critical aspect for many tactile Internet applications dependent on real-time and accurate object recognition.