Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

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.

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This work is supported by the Academy of Finland (projects no._{287150, 2015-2019, and no.}310779, 2017-2021) and European Union’s H2020 Framework Programme (H2020-MSCA-ITN-2014) under grant agreement no.~642685 MacSeNet.

The binary components GroupProcessor and BlockMatch present in the distribution were provided by the authors of BM3D.