  Image acquisition in low-light conditions suffers from poor quality and
significant degradation in visual aesthetics. This affects the visual
perception of the acquired image and the performance of various computer vision
and image processing algorithms applied after acquisition. Especially for
videos, the additional temporal domain makes it more challenging, wherein we
need to preserve quality in a temporally coherent manner. We present a simple
yet effective approach for low-light image and video enhancement. To this end,
we introduce "Adaptive Chromaticity", which refers to an adaptive computation
of image chromaticity. The above adaptivity allows us to avoid the costly step
of low-light image decomposition into illumination and reflectance, employed by
many existing techniques. All stages in our method consist of only point-based
operations and high-pass or low-pass filtering, thereby ensuring that the
amount of temporal incoherence is negligible when applied on a per-frame basis
for videos. Our results on standard lowlight image datasets show the efficacy
of our algorithm and its qualitative and quantitative superiority over several
state-of-the-art techniques. For videos captured in the wild, we perform a user
study to demonstrate the preference for our method in comparison to
state-of-the-art approaches.
