基于梯度域引导滤波和显著性分析的红外与可见光图像融合

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

针对传统多尺度融合方法不能突出目标信息、融合图像缺失细节与纹理的问题,提出一种基于梯度域引导滤波和显著性检测的红外与可见光图像融合方法。该方法利用梯度域引导滤波将输入图像分解为基础层和细节层,同时利用加权的全局对比度方法将基础层分解为特征层以及差异层。在融合过程中,分别采用相位一致性组合加权局部能量、局部熵结合加权最小二乘优化、平均规则来融合特征层、差异层、细节层。实验结果表明,所提融合方法的多项指标相对于其他方法提升较多,且图像视觉效果更好,在突出目标信息、保留轮廓细节、提高对比度和清晰度方面十分有效。

Traditional multiscale 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 domainguided filtering and saliency detection is proposed.This method utilizes gradient domainguided 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.