  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 image enhancement techniques almost impossible
to apply. Very recently, researchers have shown promising results using
learning based approaches. Motivated by these ideas, in this paper, we propose
a deep learning framework for obtaining clean and colorful RGB images from
extremely dark raw images. The backbone of our framework is a novel
coarse-to-fine network architecture that generates high-quality outputs in a
progressive manner. 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 noise and improve 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 the proposed approach
leads to perceptually more pleasing results than state-of-the-art methods by
producing much sharper and higher quality images.
