EasyFuse: Easy-to-learn visible and infrared image fusion framework based on unpaired set

Abstract

The major hurdle in building a visible and infrared fusion model is the necessity of large-scale pixel-aligned and time-synchronized image pairs. In this paper, we propose an easy-to-learn visible and infrared image fusion framework that does not require pairs for training. In addition to easy training using unpaired sets, our framework provides fusion images with more textures and meaningful scene information compared to previous works. In particular, to mix features from each spectrum, we newly present a feature line-up module to identify important information in each source. Additionally, in order to provide a new option for benchmark evaluation, we construct a new sequence-based visible and infrared paired dataset that is aligned and synchronized. Finally, we perform extensive experiments to verify the performance of the proposed method by both public and proposed datasets.