  Visible and near-infrared(NIR) band sensors provide images that capture
complementary spectral radiations from a scene. And the fusion of the visible
and NIR image aims at utilizing their spectrum properties to enhance image
quality. However, currently visible and NIR fusion algorithms cannot well take
advantage of spectrum properties, as well as lack information complementarity,
which results in color distortion and artifacts. Therefore, this paper designs
a complementary fusion model from the level of physical signals. First, in
order to distinguish between noise and useful information, we use two layers of
the weight-guided filter and guided filter to obtain texture and edge layers,
respectively. Second, to generate the initial visible-NIR complementarity
weight map, the difference maps of visible and NIR are filtered by the
extend-DoG filter. After that, the significant region of NIR night-time
compensation guides the initial complementarity weight map by the arctanI
function. Finally, the fusion images can be generated by the complementarity
weight maps of visible and NIR images, respectively. The experimental results
demonstrate that the proposed algorithm can not only well take advantage of the
spectrum properties and the information complementarity, but also avoid color
unnatural while maintaining naturalness, which outperforms the
state-of-the-art.
