Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform

Authors

  • Yuxin Sun Science and Technology on Electro-Optical Information Security Control Laboratory, China
  • Yuhai Li Science and Technology on Electro-Optical Information Security Control Laboratory, China
  • Kai Feng Science and Technology on Electro-Optical Information Security Control Laboratory, China

DOI:

https://doi.org/10.47852/bonviewJCCE3202943

Keywords:

polarization image, Grey Wolf Optimization, Non-subsampled Contourlet Transform, image denoising, pulse-coupled neural network

Abstract

In this article, an adaptive pulse-coupled neural network (PCNN) polarization image denoising method based on Grey Wolf Optimization (GWO) and Non-Subsampled Contourlet Transform(NSCT) is proposed. Different from the traditional PCNN denoising method, the captured polarization image was firstly devised by the NSCT and enforced band-decomposition to denoised by PCNN. The evaluable index of the image was used for quantitative analysis. Then, GWO is used to update PCNN inherent voltage constant and attenuation time constant and neurons connected intensity factor three model parameters, after looking for multiple optimal solutions, and then to polarized image denoising to achieve the best effect. This method not only avoids the image edge blurring caused by the traditional image denoising method, but also solves the problem that the parameters of the PCNN are difficult to accurately estimate. Hence, it is more suitable for polarization images containing noise. The experiment and the quantitative analysis of image evaluation indices showed that NSCT-GWO-PCNN effectively suppresses the noise in polarization image by reducing salt-and-pepper noise while protecting edges.

 

Received: 7 April 2023 | Revised: 15 May 2023 | Accepted: 19 May 2023

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

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Published

2023-05-24

How to Cite

Sun, Y., Li, Y., & Feng, K. (2023). Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform. Journal of Computational and Cognitive Engineering. https://doi.org/10.47852/bonviewJCCE3202943

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Research Articles

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