DRF: Disentangled Representation for Visible and Infrared Image Fusion

Abstract

In this article, we propose a novel decomposition method by applying disentangled representation for visible and infrared image fusion (DRF). According to the imaging principle, we perform the decomposition depending on the source of information in the visible and infrared images. More concretely, we disentangle the images into the scene- and sensor modality (attribute)-related representations through the corresponding encoders, respectively. In this way, the unique information defined by the attribute-related representation is closer to the information captured by each type of sensor individually. Thus, the problem of inappropriate extraction of unique information can be alleviated. Then, different strategies are applied for the fusion of these different types of representations. Finally, the fused representations are fed into the pretrained generator to generate the fusion result. The qualitative and quantitative experiments on the publicly available TNO and RoadScene data sets demonstrate the comparable performance of our DRF over the state of the art in terms of both visual effect and quantitative metrics.