Class-aware fully convolutional Gaussian and Poisson denoising

We propose a fully convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising process, in which the shallow layers handle local noise statistics, while deeper layers recover edges and enhance textures. Our method advances the state of the art when trained for different noise levels and distributions (both Gaussian and Poisson). In addition, we show that making the denoiser class-aware by exploiting semantic class information boosts the performance, enhances the textures, and reduces the artifacts.