  We propose to learn a fully-convolutional network model that consists of a
Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM
structure possesses two distinctive features that are important for the noise
removal task. Firstly, each residual unit employs identity mappings as the skip
connections and receives pre-activated input in order 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, in
other words within an identity mapping module, each neuron in the last
convolution layer can observe the full receptive field of the first layer.
After being trained on the BSD400 dataset, the proposed network produces
remarkably higher numerical accuracy and better visual image quality than the
state-of-the-art when being evaluated on conventional benchmark images and the
BSD68 dataset.
