Size-Insensitive Network for Visible-Infrared Image Fusion Model

Visible-infrared image fusion is a technique that extracts information from different sensors. It could be used to enhance human visual perception of video surveillance under low-light conditions, and provide rich information for subsequent tasks. Vision Transformer (ViT) based fusion algorithms require standardizing input images to a specific height and width that could be divided into a series of blocks of fixed size. Consequently, a scaling operation must be performed on the original image, which frequently decreases the quality of fusion results. This paper proposes a visible-infrared image fusion neural network that is insensitive to input size, by first utilizing a fixed-size image pre-fusion framework to generate lossless instructive fusion results (IFRs), followed by a size-insensitive enhancing framework that refines these preliminary fused images under the guidance of IFRs. It also has potential applicability to other image fusion algorithms, like multi-focus image fusion.