  We propose a novel deep network architecture for image\\ denoising based on a
Gaussian Conditional Random Field (GCRF) model. In contrast to the existing
discriminative denoising methods that train a separate model for each noise
level, the proposed deep network explicitly models the input noise variance and
hence is capable of handling a range of noise levels. Our deep network, which
we refer to as deep GCRF network, consists of two sub-networks: (i) a parameter
generation network that generates the pairwise potential parameters based on
the noisy input image, and (ii) an inference network whose layers perform the
computations involved in an iterative GCRF inference procedure.\ We train the
entire deep GCRF network (both parameter generation and inference networks)
discriminatively in an end-to-end fashion by maximizing the peak
signal-to-noise ratio measure. Experiments on Berkeley segmentation and
PASCALVOC datasets show that the proposed deep GCRF network outperforms
state-of-the-art image denoising approaches for several noise levels.
