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Hybrid image noise reduction algorithm based on genetic ant colony and PCNN

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Abstract

Pulse Coupled Neural Network (PCNN) has gained widespread attention as a nonlinear filtering technology in reducing the noise while keeping the details of images well, but how to determine the proper parameters for PCNN is a big challenge. In this paper, a method that can optimize the parameters of PCNN by combining the genetic algorithm (GA) and ant colony algorithm is proposed, which named as GACA, and the optimized procedure is named as GACA-PCNN. Firstly, the noisy image is filtered by median filter in the proposed GACA-PCNN method; then, the noisy image is filtered by GACA-PCNN constantly and the median filtering image is used as a reference image; finally, a set of parameters of PCNN can be automatically estimated by GACA, and the pretty effective denoising image will be obtained. Experimental results indicate that GACA-PCNN has a better performance on PSNR (peak signal noise rate) and a stronger capacity of preserving the details than previous denoising techniques.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation (61603353), the Research Project Supported by Shanxi Scholarship Council of China (2015-082), the College Funding of North University of China (110246).

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Authors and Affiliations

  1. School of Instrument and Electronics, North University of China, No.3 Xueyuan Road, Taiyuan, 030051, China

    Chong Shen, Ding Wang, Huiliang Cao & Jun Liu

  2. National Key Laboratory for Electronic Measurement Technology, Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education, School of Instrument and Electronics, North University of China, No.3 Xueyuan Road, Taiyuan, 030051, China

    Chong Shen, Huiliang Cao & Jun Liu

  3. High-Tech Innovation Engineering Center, Institute of Automation, University of Chinese Academy of Science, No.95 Zhongguancun East Road, Beijing, 100190, China

    Shuming Tang

Authors
  1. Chong Shen
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  2. Ding Wang
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  3. Shuming Tang
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  4. Huiliang Cao
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  5. Jun Liu
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Correspondence to Jun Liu.

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Shen, C., Wang, D., Tang, S. et al. Hybrid image noise reduction algorithm based on genetic ant colony and PCNN. Vis Comput 33, 1373–1384 (2017). https://doi.org/10.1007/s00371-016-1325-x

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