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The Method for Image Noise Detection Based on the Amount of Knowledge Associated with Intuitionistic Fuzzy Sets

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Web and Big Data. APWeb-WAIM 2021 International Workshops (APWeb-WAIM 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1505))

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Abstract

The existing methods of two-stage fuzzy noise detection are all based on intuitionistic fuzzy entropy (IFE) using only the aspect of hesitation margin in the context of intuitionistic fuzzy sets (IFSs). However, there are great limitations of this type of method due to the inherent shortcomings of IFE, thus leading to limited accuracy of detecting noise. To solve this problem, we introduce in this paper the intuitionistic fuzzy knowledge measure (IFKM) into this fuzzy noise detection stage, in which the IFKM plays an important role. The fuzzification of an image at each grey level is implemented first by establishing two classes of IFSs for the foreground and background of the noisy images. The maximum amount of knowledge is then calculated, with which to determine the optimal threshold using the membership function values of the foreground and background of the noisy images, respectively. The noise pixels are detected with a noise membership function constructed by the average intensity of foreground background and each pixel value of noisy images, and removed with the iterative mean filter (IMF). By experiment, we use such performance metrics as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Visual Information Fidelity (VIF) and Image Enhancement Factor (IEF) to assess image quality. Furthermore, we compare denoising results of the proposed method with other state-of-the-art methods. The qualitative results show that the proposed method outperforms these algorisms. This paper applies the latest IFKM theory to the field of image noise detection for the first time, which also creates a new example for exploring the potential application areas of the theory.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grant No. 71771110, and in part by the Planning Research Foundation of Social Science of the Ministry of Education of China under Grant No. 16YJA630014.

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Authors and Affiliations

  1. Liaoning University, Shenyang, 110036, China

    Kaihong Guo & Yongzhi Zhou

Authors
  1. Kaihong Guo
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  2. Yongzhi Zhou
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Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Yunjun Gao

  2. Soochow University, Suzhou, China

    An Liu

  3. University of Southern Queensland, Toowoomba, QLD, Australia

    Xiaohui Tao

  4. South China University of Technology, Guangzhou, China

    Junying Chen

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Guo, K., Zhou, Y. (2021). The Method for Image Noise Detection Based on the Amount of Knowledge Associated with Intuitionistic Fuzzy Sets. In: Gao, Y., Liu, A., Tao, X., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021 International Workshops. APWeb-WAIM 2021. Communications in Computer and Information Science, vol 1505. Springer, Singapore. https://doi.org/10.1007/978-981-16-8143-1_6

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  • DOI: https://doi.org/10.1007/978-981-16-8143-1_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8142-4

  • Online ISBN: 978-981-16-8143-1

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