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

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.