  Capturing images under extremely low-light conditions poses significant
challenges for the standard camera pipeline. Images become too dark and too
noisy, which makes traditional enhancement techniques almost impossible to
apply. Recently, learning-based approaches have shown very promising results
for this task since they have substantially more expressive capabilities to
allow for improved quality. Motivated by these studies, in this paper, we aim
to leverage burst photography to boost the performance and obtain much sharper
and more accurate RGB images from extremely dark raw images. The backbone of
our proposed framework is a novel coarse-to-fine network architecture that
generates high-quality outputs progressively. The coarse network predicts a
low-resolution, denoised raw image, which is then fed to the fine network to
recover fine-scale details and realistic textures. To further reduce the noise
level and improve the color accuracy, we extend this network to a permutation
invariant structure so that it takes a burst of low-light images as input and
merges information from multiple images at the feature-level. Our experiments
demonstrate that our approach leads to perceptually more pleasing results than
the state-of-the-art methods by producing more detailed and considerably higher
quality images.
