  We introduce a paradigm for nonlocal sparsity reinforced deep convolutional
neural network denoising. It is a combination of a local multiscale denoising
by a convolutional neural network (CNN) based denoiser and a nonlocal denoising
based on a nonlocal filter (NLF) exploiting the mutual similarities between
groups of patches. CNN models are leveraged with noise levels that
progressively decrease at every iteration of our framework, while their output
is regularized by a nonlocal prior implicit within the NLF. Unlike complicated
neural networks that embed the nonlocality prior within the layers of the
network, our framework is modular, it uses standard pre-trained CNNs together
with standard nonlocal filters. An instance of the proposed framework, called
NN3D, is evaluated over large grayscale image datasets showing state-of-the-art
performance.
