In multi-scale geometric analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and non-subsampled shearlet transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the wavelet transform (WT) is used to decompose high-frequency sub-bands to obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

在基于多尺度几何分析（MGA）的红外和可见光图像融合方法中，对两种类型的图像采用相同的表示形式将导致融合图像中的热辐射目标不明显，这很难与的背景。为了解决该问题，提出了一种基于非线性增强和非下采样的小波变换（NSST）分解的融合算法。首先，使用NSST将两个源图像分解为低频和高频子带。然后，使用小波变换（WT）分解高频子带，以获得近似子带和方向细节子带。执行“平均”融合规则以融合近似子带。并且执行“最大绝对”融合规则以用于方向细节子带的融合。反向WT被用于重构高频子带。为了突出热辐射目标，我们构造了一个非线性变换函数来确定低频子带的融合权重，并且可以进一步调整其参数以满足不同的融合要求。最后，反NSST用于重建融合图像。实验结果表明，所提出的方法可以同时增强红外图像中的热目标，并保留可见图像中的纹理细节，并且在视觉和视觉上都可以与现有的融合方法竞争甚至优于后者。和定量评估。我们构造了一个非线性变换函数来确定低频子带的融合权重，并且可以进一步调整其参数以满足不同的融合要求。最后，反NSST用于重建融合图像。实验结果表明，所提出的方法可以同时增强红外图像中的热目标，并保留可见图像中的纹理细节，并且在视觉和视觉上都可以与现有的融合方法竞争甚至优于后者。和定量评估。我们构造了一个非线性变换函数来确定低频子带的融合权重，并且可以进一步调整其参数以满足不同的融合要求。最后，反NSST用于重建融合图像。实验结果表明，所提出的方法可以同时增强红外图像中的热目标，并保留可见图像中的纹理细节，并且在视觉和视觉上都可以与现有的融合方法竞争甚至优于后者。和定量评估。