Abstract:

 Infrared images mainly present the temperature distribution or thermal radiation characteristics of objects, while visible light images mainly present visual features such as color, texture, and geometric shape of objects. Image fusion technology extracts the information and significant features of infrared and visible light images, and fuses them to significantly enhance the recognition ability of target objects in complex scenes, and improves the accuracy and efficiency of environmental perception and decision support. Based on the researches of numerous scholars at home and abroad, this article systematically summarizes the theoretical basis, fusion methods, mainstream algorithm performance comparison, key technologies, urgent problems of pixel‑level infrared, visible light fusion through comprehensive research and induction. It shows that deep learning has significant advantages in solving core problems such as feature alignment and detail preservation in pixel‑level image fusion. The future development directions of image fusion technology, such as lightweight models, task adaptation, and cross‑modal fusion, are analyzed.

Key words: infrared and visible light images, deep learning, image fusion, fusion method, technical path