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

Infrared and Visible Light Image Fusion Based on Guided Filtering Weight and Saliency Information Optimization

为了提升不同传感器数据融合效果,提出了一种基于权重与显著性信息优化的引导滤波红外与可见光图像融合算法。首先通过高斯滤波器将图像分为基础层与细节层;其次提取目标轮廓作为显著性信息,减轻区域噪声影响;再次通过滑动窗口计算局部梯度优化权重图构建,减轻单个噪声点影响并提升权重图置信度;然后使用引导滤波处理权重图,抑制伪影去除噪声;最后选择合适的细节层与基础层融合系数配比,强化细节信息,完成融合。实验结果表明,本文算法可加强融合图像在纹理细节上的表现,在信息熵、平均梯度、盲图像质量等主要融合评价指标方面,相较于选取的5个具有代表性融合算法有一定程度的提升。

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.