Infrared and visible image fusion based on particle swarm optimization and dense block

Abstract

Infrared and visible image fusion aims to preserve essential thermal information and crucial visible details from two types of input images to generate an informative fusion image for better visual perception. In recent years, several hybrid methods have been applied in the field of infrared and visible image fusion. In this paper, we proposed a novel image fusion method based on particle swarm optimization and dense block for visible and infrared images. Particle swarm optimization is utilized to optimize the weighting factors of the coefficients obtained by discrete wavelet transform, then the coefficients are fused with the optimum weight to obtain the initial fusion image. The final fusion image is created by integrating the first fused image with the input visible image using a deep learning model, in which dense block is utilized for better feature extraction ability. The results of comparison experiments demonstrate that our method produces fusion images with richer details and texture features, and the fused image reduces the artifacts and noise.