Research on Efficient Algorithms of Deep Learning in Visible and Infrared Light Image Fusion

Abstract

Image fusion plays a vital method that integrates data from infrared (IR) and visible light images, to improve visual data quality for specific applications, like, surveillance and object detection. This research investigates efficient algorithms for deep learning (DL)-based image fusion, in the concept of architectures that utilize the complementary characteristics of IR and visible light images. It employs data comprising IR and visible light images captured under varying environmental conditions, ensuring a diverse representation of scenarios. The images perform pre-processing methods such as noise reduction and histogram equalization to optimize the fusion quality. Multi-scale feature extraction is used for feature extraction. This research presents a novel approach leveraging a synergistic fibroblast fine-tuned efficient convolutional neural network (SF-ECNN) for fusion. The SF-ECNN model is analyzed using various metrics and evaluations. Evaluation metrics reveal an EN value of 7.2368, VIF of 0.8124, SD of 95.3515, SF of 19.5314, and AG of 7.3571. Thus, the outcomes exhibit significant improvements in comparison to current advanced fusion techniques, underscoring the efficacy and efficiency of the methodology in producing fused images of superior quality.