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Low-density noise removal based on lambda multi-diagonal matrix filter for binary image

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Abstract

Binary image denoising is a well-known problem, and it is the concern of diverse application areas. Many classical denoising techniques have evolved over the years, such as mean filter, median filter, and morphological filter. A new denoising method based on the multiplication of lambda multi-diagonal binary matrix (\(\lambda \)-MDBM) is proposed for binary images in this paper. In proposed method, first the users need to choose an appropriate lambda value from the set \(\{\lambda |\lambda \in [0.5,1]\}\), and then the hybrid noisy binary image matrix can be obtained by adding the noisy binary image matrix times the \(\lambda \)-MDBM and the transpose of the noisy binary image matrix times the \(\lambda \)-MDBM, and finally the de-noised binary image can be obtained by a preset threshold. The experimental results show that a new denoising method based on \(\lambda \)-MDBM is possible in peak signal-to-noise ratio and mean square error.

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Acknowledgments

The authors would like to thank the anonymous referees and the editor for their valuable opinions. And this work is partially supported by the 111 Project under Grant No. B12018, PAPD of Jiangsu Higher Education Institutions, and the Graduates’ Research Innovation Program of Higher Education of Jiangsu Province of China under Grant No. CXZZ13-0239.

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Ministry of Education, Wuxi, 214122, China

    Li Li & Hongwei Ge

  2. School of Internet of Things, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Li Li, Hongwei Ge & Yixin Zhang

  3. College of Computer and Information, Hohai University, Nanjing, 210098, China

    Jianqiang Gao

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  1. Li Li
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  2. Hongwei Ge
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  3. Yixin Zhang
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  4. Jianqiang Gao
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Correspondence to Jianqiang Gao.

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Li, L., Ge, H., Zhang, Y. et al. Low-density noise removal based on lambda multi-diagonal matrix filter for binary image. Neural Comput & Applic 29, 173–185 (2018). https://doi.org/10.1007/s00521-016-2538-7

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  • DOI: https://doi.org/10.1007/s00521-016-2538-7

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