  Images captured underwater often suffer from suboptimal illumination settings
that can hide important visual features, reducing their quality. We present a
novel single-image low-light underwater image enhancer, L^2UWE, that builds on
our observation that an efficient model of atmospheric lighting can be derived
from local contrast information. We create two distinct models and generate two
enhanced images from them: one that highlights finer details, the other focused
on darkness removal. A multi-scale fusion process is employed to combine these
images while emphasizing regions of higher luminance, saliency and local
contrast. We demonstrate the performance of L^2UWE by using seven metrics to
test it against seven state-of-the-art enhancement methods specific to
underwater and low-light scenes. Code available at:
https://github.com/tunai/l2uwe.

Computer Vision and Pattern Recognition