Abstract
We propose a novel deep network architecture for
grayscale and color image denoising that is based on a
non-local image model. Our motivation for the overall design of the proposed network stems from variational methods that exploit the inherent non-local self-similarity property of natural images. We build on this concept and introduce deep networks that perform non-local processing and
at the same time they significantly benefit from discriminative learning. Experiments on the Berkeley segmentation
dataset, comparing several state-of-the-art methods, show
that the proposed non-local models achieve the best reported denoising performance both for grayscale and color
images for all the tested noise levels. It is also worth noting
that this increase in performance comes at no extra cost on
the capacity of the network compared to existing alternative
deep network architectures. In addition, we highlight a direct link of the proposed non-local models to convolutional
neural networks. This connection is of significant importance since it allows our models to take full advantage of
the latest advances on GPU computing in deep learning and
makes them amenable to efficient implementations through
their inherent parallelism.