Abstract
We design a novel network architecture for learning discriminative image models that are employed to efficiently
tackle the problem of grayscale and color image denoising. Based on the proposed architecture, we introduce two
different variants. The first network involves convolutional
layers as a core component, while the second one relies instead on non-local filtering layers and thus it is able to exploit the inherent non-local self-similarity property of natural images. As opposed to most of the existing deep network
approaches, which require the training of a specific model
for each considered noise level, the proposed models are
able to handle a wide range of noise levels using a single set
of learned parameters, while they are very robust when the
noise degrading the latent image does not match the statistics of the noise used during training. The latter argument
is supported by results that we report on publicly available
images corrupted by unknown noise and which we compare
against solutions obtained by competing methods. At the
same time the introduced networks achieve excellent results
under additive white Gaussian noise (AWGN), which are
comparable to those of the current state-of-the-art network,
while they depend on a more shallow architecture with the
number of trained parameters being one order of magnitude
smaller. These properties make the proposed networks ideal
candidates to serve as sub-solvers on restoration methods
that deal with general inverse imaging problems such as
deblurring, demosaicking, superresolution, etc