Abstract：In order to improve the fusion effect of different sensors, a guided filtering infrared and visible image fusion algorithm based on weight and saliency information optimization is proposed in this paper. First, the image is divided into a base layer and a detail layer through Gaussian filter. Second, the target contour is extracted as saliency information to reduce the influence of regional noise. Third, the local gradient is calculated through a sliding window to optimize the weight map construction to reduce the influence of noise points and increase the confidence of the weight map. Fourth, the guided filtering is used to process the coarse weight map to suppress artifacts and remove noise. Finally, the appropriate detail layer and base layer fusion coefficient ratio is selected to strength the detail information and complete the fusion. The experimental results show that the algorithm in this paper strengthens the performance of the fusion image in texture details, and has a certain degree of improvement in the main fusion evaluation indicators such as information entropy, average gradient, and blind image quality, compared with the selected five commonly used fusion algorithms.
杨擎宇, 宋泉宏, 魏志飞, 顾一凡. 基于引导滤波权重与显著信息优化的红外与可见光图像融合[J]. 空天防御, 2021, 4(4): 113-118.
YANG Qingyu, SONG Quanhong, WEI Zhifei, GU Yifan. Infrared and Visible Light Image Fusion Based on Guided Filtering Weight and Saliency Information Optimization. Air & Space Defense, 2021, 4(4): 113-118.
