Infrared and Visible Image Fusion Based on Contrast Enhancement and Multi-scale Edge-preserving Decomposition

To improve the contrast of details, an infrared and visible image fusion method based on contrast enhancement and multi-scale edge-preserving decomposition is proposed. A multi-scale contrast adaptive enhancement algorithm is used to improve the visibility of visible images before the fusion. The contrast of details can be effectively enhanced with this method. Then, fusion rules are designed according to the properties of different layers to preserve the restored information as much as possible. In order to retain more texture information of source images, the parameter-adaptive model is applied to fuse the small-scale detail layer. In order to avoid mutual interference of different characteristic images, a new edge detection method is used to fuse large-scale detail layer. In order to improve the contrast of fusion results, the weighted averaging technique based on visual saliency map is used to fuse the basic layer. Finally, a clear image is obtained by combining the fused layers. The experimental results show that the proposed method has better visual effect than other fusion methods.