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Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

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Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut1216-1220
Sivumäärä5
JulkaisuIEEE Signal Processing Letters
Vuosikerta25
Numero8
DOI - pysyväislinkit
TilaJulkaistu - 1 elokuuta 2018
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

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, and it uses standard pretrained 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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