针对可见光和红外图像进行图像融合时，红外图像细节信息丢失严重、边缘模糊和可见光图像对比度不足等问题，提出一种结合非下采样轮廓波变换（Non-subsampled contourlet transform，NSCT）与引导滤波的图像融合方法。首先采用模糊逻辑算法对可见光图像进行增强提高其对比度，突出图像的有效信息；再对增强后的可见光和红外图像进行NSCT 分解得到低频与高频子带；然后对红外图像的高频子带采用改进后的引导滤波增强边缘等细节信息；其次使用平均梯度策略和模糊逻辑策略对高、低频子带进行融合；最后利用NSCT 逆变换得到融合后的图像。通过在不同数据集上的实验结果表明，该方法在信息熵、标准差和互信息等评价指标上均要优于其他几种方法，验证了本文所提方法的有效性和优越性。

To mitigate the problems of serious image information loss, blurred edges, and insufficient contrast of visible images, a novel image fusion method based on non-subsampled contourlet transform and guided filtering is proposed for the image fusion of visible and infrared images. First, a fuzzy logic algorithm is used to enhance the contrast of the visible image to highlight the effective information of the image. Subsequently, the NSCT decomposition of the enhanced visible and infrared images is performed to obtain the low-frequency and high-frequency sub-bands. Further, the high-frequency sub-band of the infrared image is adopted to improved edge filtering and other information, Next, the average gradient strategy and fuzzy logic strategy are used to fuse the high- and low-frequency sub-bands. Finally, theNSCT inverse transform is used to obtain the fused image. The experimental results on different data sets demonstrate that the proposed method is superior to other methods in evaluating the entropy, standard deviation, and mutual information, all of which verify the effectiveness and superiority of the proposed method.