Image fusion methods in high-speed railway scenes: A survey

Abstract

Image fusion refers to extracting meaningful information from images of different sources or modalities, and then fusing them to generate more informative images that are beneficial for subsequent applications. In recent years, the growing data and computing resources have promoted the development of deep learning, and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods. However, high-speed railroads, as an important part of life, have their unique industry characteristics of image data, which leads to different image fusion techniques with different fusion effects in high-speed railway scenes. This research work first introduces the mainstream technology classification of image fusion, further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes, and introduces the evaluation metrics of image fusion, followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes, and finally provides some possible future image fusion in the field of rail transportation of research.