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Research on Image Signal Identification Based on Adaptive Array Stochastic Resonance

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Abstract

Aiming at the problems of low accuracy of image signal identification and poor anti-noise signal interference ability under strong noise environment, a signal identification method of correlated noisy image based on adaptive array stochastic resonance (SR) is proposed in this paper. Firstly, the two-dimensional grayscale image is transformed to a one-dimensional binary pulse amplitude modulation (BPAM) signal with periodicity by the row or column scanning method, encoding and modulation. Then, the one-dimensional low signal-to-noise ratio BPAM signal can be applied to the saturating nonlinearity array SR module for image signal identification processing and part of the noise energy is converted into signal energy. Finally, the one-dimensional image signal processed by the nonlinearities is demodulated, decoded and reverse scanned to get the restored grayscale image. The simulation results show that the image signal identification method proposed in this paper is highly efficient and accurate for the identification of noisy image signals of different sizes, and the bit error rate (BER) is also significantly reduced.

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Authors and Affiliations

  1. College of Computer Science & Technology, Qingdao University, Qingdao, 266071, China

    Jingjing Zhao, Yumei Ma, Zhenkuan Pan & Huage Zhang

Authors
  1. Jingjing Zhao
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  2. Yumei Ma
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  3. Zhenkuan Pan
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  4. Huage Zhang
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Corresponding author

Correspondence to Yumei Ma.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 61501276, 61573204, 61772294 and 61973179, the China Postdoctoral Science Foundation under Grant No. 2016M592139, and the Qingdao Postdoctoral Applied Research Project under Grant No. 2015120.

This paper was recommended for publication by Editor GUO Jin.

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Cite this article

Zhao, J., Ma, Y., Pan, Z. et al. Research on Image Signal Identification Based on Adaptive Array Stochastic Resonance. J Syst Sci Complex 35, 179–193 (2022). https://doi.org/10.1007/s11424-021-0133-1

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  • DOI: https://doi.org/10.1007/s11424-021-0133-1

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