  We propose to learn a fully-convolutional network model that consists of a
Chain of Identity Mapping Modules and residual on the residual architecture for
image denoising. Our network structure possesses three distinctive features
that are important for the noise removal task. Firstly, each unit employs
identity mappings as the skip connections and receives pre-activated input to
preserve the gradient magnitude propagated in both the forward and backward
directions. Secondly, by utilizing dilated kernels for the convolution layers
in the residual branch, each neuron in the last convolution layer of each
module can observe the full receptive field of the first layer. Lastly, we
employ the residual on the residual architecture to ease the propagation of the
high-level information. Contrary to current state-of-the-art real denoising
networks, we also present a straightforward and single-stage network for real
image denoising. The proposed network produces remarkably higher numerical
accuracy and better visual image quality than the classical state-of-the-art
and CNN algorithms when being evaluated on the three conventional benchmark and
three real-world datasets.
