  Low-light image sequences generally suffer from spatio-temporal incoherent
noise, flicker and blurring of moving objects. These artefacts significantly
reduce visual quality and, in most cases, post-processing is needed in order to
generate acceptable quality. Most state-of-the-art enhancement methods based on
machine learning require ground truth data but this is not usually available
for naturally captured low light sequences. We tackle these problems with an
unpaired-learning method that offers simultaneous colorization and denoising.
Our approach is an adaptation of the CycleGAN structure. To overcome the
excessive memory limitations associated with ultra high resolution content, we
propose a multiscale patch-based framework, capturing both local and contextual
features. Additionally, an adaptive temporal smoothing technique is employed to
remove flickering artefacts. Experimental results show that our method
outperforms existing approaches in terms of subjective quality and that it is
robust to variations in brightness levels and noise.
