We present an overview of our recent progress and the current state-of-the-art techniques of color image fusion for night vision applications. Inspired by previously developed color opponent fusing schemes, we initially developed a simple pixel-based false color-mapping scheme that yielded fused false color images with large color contrast and preserved the identity of the input signals. This method has been successfully deployed in different areas of research. However, since this color mapping did not produce realistic colors, we continued to develop a statistical color-mapping procedure that would transfer the color distribution of a given example image to a multiband nighttime image. This procedure yields a realistic color rendering. However, it is computationally expensive and achieves no color constancy since the mapping depends on the relative amounts of the different materials in the scene. By applying the statistical mapping approach in a color look-up-table framework, we finally achieved both color constancy and computational simplicity. This sample-based color transfer method is specific for different types of materials in a scene and can be easily adapted for the intended operating theatre and the task at hand. The method can be implemented as a look-up-table transform and is highly suitable for real-time implementations.