  Poisson distribution is used for modeling noise in photon-limited imaging.
While canonical examples include relatively exotic types of sensing like
spectral imaging or astronomy, the problem is relevant to regular photography
now more than ever due to the booming market for mobile cameras. Restricted
form factor limits the amount of absorbed light, thus computational
post-processing is called for. In this paper, we make use of the powerful
framework of deep convolutional neural networks for Poisson denoising. We
demonstrate how by training the same network with images having a specific peak
value, our denoiser outperforms previous state-of-the-art by a large margin
both visually and quantitatively. Being flexible and data-driven, our solution
resolves the heavy ad hoc engineering used in previous methods and is an order
of magnitude faster. We further show that by adding a reasonable prior on the
class of the image being processed, another significant boost in performance is
achieved.
