  Enhancing the visibility in extreme low-light environments is a challenging
task. Under nearly lightless condition, existing image denoising methods could
easily break down due to significantly low SNR. In this paper, we
systematically study the noise statistics in the imaging pipeline of CMOS
photosensors, and formulate a comprehensive noise model that can accurately
characterize the real noise structures. Our novel model considers the noise
sources caused by digital camera electronics which are largely overlooked by
existing methods yet have significant influence on raw measurement in the dark.
It provides a way to decouple the intricate noise structure into different
statistical distributions with physical interpretations. Moreover, our noise
model can be used to synthesize realistic training data for learning-based
low-light denoising algorithms. In this regard, although promising results have
been shown recently with deep convolutional neural networks, the success
heavily depends on abundant noisy clean image pairs for training, which are
tremendously difficult to obtain in practice. Generalizing their trained models
to images from new devices is also problematic. Extensive experiments on
multiple low-light denoising datasets -- including a newly collected one in
this work covering various devices -- show that a deep neural network trained
with our proposed noise formation model can reach surprisingly-high accuracy.
The results are on par with or sometimes even outperform training with paired
real data, opening a new door to real-world extreme low-light photography.
