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Salt and Pepper Noise Suppression for Medical Image by Using Non-local Homogenous Information

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Cognitive Internet of Things: Frameworks, Tools and Applications (ISAIR 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 810))

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Abstract

In this paper, we propose a method to suppress salt and pepper noise for medical images based on the homogenous information obtained by non-symmetrical and anti-packing model (NAM). The NAM could divide the image into several homogenous blocks and it is sensitive to the additive extra energy. Thus the noise could be detected effectively due to the usage of bit-plane during the division. Then corrupted points are estimated by using a distance based weighted mean filter according to the homogenous information in its non-local region, which could keep local structure. Experimental results show that our method can obtain denoising results with high quality.

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Acknowledgements

This work is supported by NSFC (No. 61802213) and Shandong Provincial Natural Science Found (No. ZR2017LF016, ZR2018LF004).

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Authors and Affiliations

  1. College of Information, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China

    Hu Liang & Shengrong Zhao

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  1. Hu Liang
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  2. Shengrong Zhao
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Correspondence to Shengrong Zhao .

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Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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Liang, H., Zhao, S. (2020). Salt and Pepper Noise Suppression for Medical Image by Using Non-local Homogenous Information. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_19

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