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Edge Testing of Noisy Image Based on Wavelet Neural Network

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Abstract

To perfect inspecting effectiveness for image edge with noise, wavelet neural network is used for executing inspection of image edge. Basic theory of image noise is analyzed firstly, and the edge of images with Gaussian noise and Rayleigh noise is processed based on MATLAB software. The basic theory of wavelet neural network is analyzed, and framework of wavelet neural network is designed, the mathematical expressions of hidden layer are established. And an improved genetic algorithm is designed to carry out optimization for the parameters of wavelet neural network, the fitness degree function is established, and the analysis flowchart of algorithm is designed. Numerical analysis on testing for image edge concluding noise is implemented, analysis results illustrate that the proposed model is an effective tool for testing the edge of image concluding noise.

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Authors and Affiliations

  1. Department of Control Technology, Wuxi Institute of Technology, 214000, Jiangsu, China

    Aodong Zhao &  Nan Zhang

Authors
  1. Aodong Zhao
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  2. Nan Zhang
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Correspondence to Aodong Zhao.

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The authors declare that they have no conflicts of interest.

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Aodong Zhao, Nan Zhang Edge Testing of Noisy Image Based on Wavelet Neural Network. Aut. Control Comp. Sci. 57, 61–69 (2023). https://doi.org/10.3103/S014641162301011X

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  • DOI: https://doi.org/10.3103/S014641162301011X

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