针对红外与可见光图像融合过程中细节信息丢失过多、融合结果纹理不清晰、对比度不高等问题，提出一种基于图像增强和二次非下采样轮廓波变换（NSCT）分解的红外与可见光图像融合方法。首先，对可见光图像采用基于引导滤波的图像增强算法提升图像可视性。其次，对增强后的可见光图像和红外图像分别进行NSCT分解得到低频子带和高频子带，并且在不同子带间使用不同的融合规则，得出一次融合图像的NSCT系数。然后，对一次融合图像的NSCT系数重构再分解为高频子带和低频子带并分别与可见光图像的高低频子带融合得到二次融合图像的NSCT系数。最后，对二次融合图像的NSCT系数进行逆变换重构得到最终的融合图像。利用公共数据集进行大量试验，使用8种评估指标，与8种基于多尺度的融合方法对比。实验结果表明：所提方法能保留更多源图像中的细节信息，还能提高融合结果的边缘轮廓清晰度、整体对比度，在主观视觉和评价指标上都存在优势。

To address the problems of excessive loss of detail information, unclear texture, and low contrast during the fusion of infrared and visible images, this study proposes an infrared and visible image fusion method based on image enhancement and secondary nonsubsampled contourlet transform (NSCT) decomposition. First, an image enhancement algorithm based on guided filtering is used to improve the visibility of visible images. Second, the enhanced visible and infrared images are decomposed by NSCT to obtain low- and high-frequency subbands, and different fusion rules are used in different subbands to obtain the NSCT coefficient of the first fusion image. The NSCT coefficients of the primary fused image are reconstructed and decomposed into low- and high-frequency subbands, which are then fused with the low- and high-frequency subbands of the visible light image, respectively to obtain the NSCT coefficients of the secondary fused image. Finally, the NSCT coefficients of the secondary fused image are reconstructed by inverse transformation to obtain the final fused image. Numerous experiments are conducted with public datasets, using eight evaluation indicators to compare the proposed method with eight fusion methods based on multiple scales. Results show that the proposed method can retain more details of the source image, improve the edge contour definition and overall contrast of the fusion results, and has advantages in terms of subjective vision and the use of evaluation indicators.