Convolutional neural network-based detector for random-valued impulse noise

Shaoping Xu; Guizhen Zhang; Lingyan Hu; Tingyun Liu

doi:10.1117/1.JEI.27.5.050501

15 October 2018 Convolutional neural network-based detector for random-valued impulse noise

Shaoping Xu, Guizhen Zhang, Lingyan Hu, Tingyun Liu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 050501 (October 2018). https://doi.org/10.1117/1.JEI.27.5.050501

Abstract

A shallow yet effective convolutional neural network (CNN)-based detector was proposed for automatic detection of random-valued impulse noise (RVIN) from images. We guided the proposed CNN-based detector to automatically extract the implicit statistics and learn the detection mechanism with a large number of patches and their corresponding noise labels regarding center pixels. Compared with the reference RVIN detectors, the proposed CNN-based one takes advantage of the prior knowledge obtained in the training phase and shows impressive detection accuracy across different noise ratios.

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Shaoping Xu, Guizhen Zhang, Lingyan Hu, and Tingyun Liu "Convolutional neural network-based detector for random-valued impulse noise," Journal of Electronic Imaging 27(5), 050501 (15 October 2018). https://doi.org/10.1117/1.JEI.27.5.050501

Received: 5 May 2018; Accepted: 12 September 2018; Published: 15 October 2018

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