  The increasing demand for high image quality in mobile devices brings forth
the need for better computational enhancement techniques, and image denoising
in particular. At the same time, the images captured by these devices can be
categorized into a small set of semantic classes. However simple, this
observation has not been exploited in image denoising until now. In this paper,
we demonstrate how the reconstruction quality improves when a denoiser is aware
of the type of content in the image. To this end, we first propose a new fully
convolutional deep neural network architecture which is simple yet powerful as
it achieves state-of-the-art performance even without being class-aware. We
further show that a significant boost in performance of up to $0.4$ dB PSNR can
be achieved by making our network class-aware, namely, by fine-tuning it for
images belonging to a specific semantic class. Relying on the hugely successful
existing image classifiers, this research advocates for using a class-aware
approach in all image enhancement tasks.
