Visible (VS) and near infra-red (NIR) band sensors provide digital
images that capture complementary spectral radiations from a
scene. Since NIR radiations propagate well through haze, mist, or
fog, the captured NIR image contains better scene details compared
to the VS image in such cases. However, NIR radiations are material
dependent and provide little information about color or texture of
the scene’s objects. To exploit the complementary details provided
by VS and NIR images, we propose a fusion approach that adaptively
injects missing spatial details to the VS image from the NIR
image while preserving the spectral contents of the VS image. The
spatial details are adaptively weighted based on the relative difference
between local contrasts of the NIR and the VS images. Thus,
the proposed approach prevents unnecessary modification of colors
or amplification of scene details that result in an unrealistic fused
image. Moreover, the proposed approach is non-iterative, fast with
a low complexity of O(n), and suitable to be implemented on embedded
cameras’ hardware. Experimental fusion results obtained on
natural NIR and VS image pairs show the effectiveness of the proposed
approach compared with two alternatives.