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, Volume 78, Issue 21, pp 30707–30721 | Cite as

A convolutional neural networks denoising approach for salt and pepper noise

  • Bo FuEmail author
  • Xiaoyang Zhao
  • Yi Li
  • Xianghai Wang
  • Yonggong Ren
Article
  • 194 Downloads

Abstract

The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.

Keywords

Image denoising Salt and pepper noise Convolutional neural networks Non-local switching filter 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) No. 61702246, Liaoning Province of China General Project of Scientific Research No. L2015285, Liaoning Province of China Doctoral Research Start-Up Fund No. 201601243.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianChina

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