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Research and application of composite stochastic resonance in enhancement detection |
| Rui Gao(高蕊)1,2, Shangbin Jiao(焦尚彬)1,3,†, and Qiongjie Xue(薛琼婕)1,3 |
1 School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China;
2 School of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China;
3 Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China |
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Abstract Aiming at the problem that the intermediate potential part of the traditional bistable stochastic resonance model cannot be adjusted independently, a new composite stochastic resonance (NCSR) model is proposed by combining the Woods—Saxon (WS) model and the improved piecewise bistable model. The model retains the characteristics of the independent parameters of WS model and the improved piecewise model has no output saturation, all the parameters in the new model have no coupling characteristics. Under α stable noise environment, the new model is used to detect periodic signal and aperiodic signal, the detection results indicate that the new model has higher noise utilization and better detection effect. Finally, the new model is applied to image denoising, the results showed that under the same conditions, the output peak signal-to-noise ratio (PSNR) and the correlation number of NCSR method is higher than that of other commonly used linear denoising methods and improved piecewise SR methods, the effectiveness of the new model is verified.
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Received: 16 September 2023
Revised: 08 October 2023
Accepted manuscript online: 10 October 2023
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PACS:
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02.60.Cb
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(Numerical simulation; solution of equations)
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05.40.-a
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(Fluctuation phenomena, random processes, noise, and Brownian motion)
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05.40.Fb
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(Random walks and Levy flights)
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05.45.-a
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(Nonlinear dynamics and chaos)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 62371388) and the Key Research and Development Projects in Shaanxi Province, China (Grant No. 2023-YBGY-044). |
Corresponding Authors:
Shangbin Jiao
E-mail: jiaoshangbin@xaut.edu.cn
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Cite this article:
Rui Gao(高蕊), Shangbin Jiao(焦尚彬), and Qiongjie Xue(薛琼婕) Research and application of composite stochastic resonance in enhancement detection 2024 Chin. Phys. B 33 010203
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