Infrared and Visible Image Fusion Based on Gradient Domain-Guided Filtering and Significance Analysis

Traditional multi-scale fusion methods cannot highlight target information and often miss details and textures in fusion images. Therefore, an infrared and visible light image fusion method based on gradient domain-guided filtering and saliency detection is proposed. This method utilizes gradient domain-guided filtering to decompose the input image into basic and detail layers and uses a weighted global contrast method to decompose the basic layer into feature and difference layers. In the fusion process, phase consistency combined with weighted local energy, local entropy combined with weighted least squares optimization, and average rules are used to fuse feature layers, difference layers, and detail layers. The experimental results show that the multiple indicators of the proposed fusion method are significantly improved compared to those of other methods, resulting in a superior visual effect of the image. The proposed method is highly effective in highlighting target information, preserving contour details, and improving contrast and clarity.