  Seeing-in-the-dark is one of the most important and challenging computer
vision tasks due to its wide applications and extreme complexities of
in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1)
RGB-dependent methods restore information using degraded RGB inputs only (\eg,
low-light enhancement), 2) RGB-independent methods translate images captured
under auxiliary near-infrared (NIR) illuminants into RGB domain (\eg, NIR2RGB
translation). The latter is very attractive since it works in complete darkness
and the illuminants are visually friendly to naked eyes, but tends to be
unstable due to its intrinsic ambiguities. In this paper, we try to robustify
NIR2RGB translation by designing the optimal spectrum of auxiliary illumination
in the wide-band VIS-NIR range, while keeping visual friendliness. Our core
idea is to quantify the visibility constraint implied by the human vision
system and incorporate it into the design pipeline. By modeling the formation
process of images in the VIS-NIR range, the optimal multiplexing of a wide
range of LEDs is automatically designed in a fully differentiable manner,
within the feasible region defined by the visibility constraint. We also
collect a substantially expanded VIS-NIR hyperspectral image dataset for
experiments by using a customized 50-band filter wheel. Experimental results
show that the task can be significantly improved by using the optimized
wide-band illumination than using NIR only. Codes Available:
https://github.com/MyNiuuu/VCSD.
