Infrared and visible image fusion based on semi-global weighted least squares and guided edge-aware filters

Most of the existing image fusion methods are dedicated to extracting the respective private features in the source image to reconstruct to get a fused image that contains rich information. However, its neglect of the enhancement of texture details in the source image as well as the preservation of background structures, which makes the fusion result look less impressive. To this end, an infrared and visible image fusion method based on a semi-global weighted least squares (SGWLS) method and guided edge-aware filter (GEAF) is proposed. First, a SGWLS method is applied to decompose the source image into base and detail layers containing different scale information. Then, the detail layer are texture-enhanced by using the scale coefficients. Due to the lack of edge features in the original base layer, for this reason, a guided edge-aware filter is designed, in which the enhanced detail layer was used as the guidance image as a way to perform a quadratic feature enhancement fusion for the base layer in order to retain richer gradient information. Finally, reconstruction of the fused detail and base layers by using inverse transformation to obtain the fusion result. Compared with nine state-of-the-art fusion algorithms, a large number of experimental results demonstrate the superior performance of the proposed method in highlighting texture details and background features, as well as the obvious advantages in SF and AG metrics.