Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

    Research output: Contribution to journalArticleScientificpeer-review

    42 Citations (Scopus)
    11 Downloads (Pure)


    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.

    Original languageEnglish
    Pages (from-to)1216-1220
    Number of pages5
    JournalIEEE Signal Processing Letters
    Issue number8
    Publication statusPublished - 1 Aug 2018
    Publication typeA1 Journal article-refereed


    • BM3D
    • convolutional neural network
    • image denoising
    • nonlocal filters

    Publication forum classification

    • Publication forum level 2

    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering
    • Applied Mathematics


    Dive into the research topics of 'Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising'. Together they form a unique fingerprint.

    Cite this