Fusion of infrared and visible images with Gaussian smoothness and joint bilateral filtering iteration decomposition

Abstract

Edge‐preserving filters have been applied to Multi‐Scale Decomposition (MSD) for fusion of infrared and visible images. Traditional edge‐preserving MSDs may hardly make satisfied structural separation from details to cause fusion performance degradation. To suppress this challenge, the authors propose a novel fusion of infrared and visible images with Gaussian smoothness and joint bilateral filtering iteration decomposition (MSD‐Iteration). This method consists of three steps. First, source images are decomposed by the Gaussian smoothness and joint bilateral filtering iteration. The implementation includes the fine‐scale detail removal with Gaussian filtering, edge and structure extraction with joint bilateral filtering iteration, and detail obtaining at multi‐scales. The decomposition has edge‐preserving and scale‐aware properties to improve detail acquisition. Second, rules are designed to conduct the layer combination. For the rule of base layers, saliency maps are constructed by Laplacian and Gaussian low‐pass filters to calculate initial weight maps. A guided filter is further applied to determine final weight maps for the combination. Meanwhile, they use the regional average energy weighting to obtain decision maps at multi‐scales by constructing intensity deviation to combine detail layers. Third, they implement the reconstruction with the combined layers. Sufficient experiments are presented to evaluate MSD‐Iteration, and experimental results validate the superiority of the authors’ method.