针对夜视图像彩色融合通常存在细节信息不够丰富、颜色对比度低的问题，为了获得更为理想的彩色融合效果，提出一种新的基于非下采样剪切波变换(Non-subsampled Shearlet Transform，NSST)和颜色对比度增强的彩色融合方法。首先，分别设计基于S 函数和局部方向对比度的低频与高频融合规则，完成源可见光与红外图像在NSST 域的融合；其次，将灰度融合图像赋给Y 分量，源图像的差值信号赋给U 和V 色差分量，形成YUV 空间的伪彩色融合图像。最后，选择一幅与待上色图像具有相似颜色分布的自然日光图像作为彩色参考图像，在YUV空间对伪彩色融合图像进行颜色对比度增强的非线性色彩传递。与近年方法相比，该方法所得彩色融合效果细节信息丰富、热目标突出。将该方法运用于彩色夜视领域，可有效增强场景深度感知和目标的可探测性。

Traditional color night vision fusion methods usually suffered from the problems of blurry visual effects and the low color contrast between the target and the background, in order to obtain the more ideal color fusion effect, an improved color fusion method based on Non-subsampled Shearlet Transform (NSST) and color contrast enhancement was proposed. Firstly, NSST was employed to decompose the infrared and visible source images, respectively, and then the gray-level fusion image was obtained according to the self-adaptive fusion rules based on the S function and the local directional contrast. Secondly, the gray fusion image was assigned to the Y component, and the difference of the source images was respectively assigned to the U and V component, and then the false color fusion image was generated in YUV space. Finally, a natural daylight color image with similar color feature to the gray fusion image was selected as the reference image, meanwhile, transferring the color feature of the reference image to the false color fusion image based on the nonlinear color transfer technique in the uncorrelated YUV space, so as to enhance the color contrast of the hot target and cold background. Compared with the methods in recent years, Experimental results showed that the color fusion result based on ours contained more abundant details, and the hot target was highlighted. This method is applied to the field of color night vision that can make for enhancing the situation awareness and improve the target detectability.