Application of ACO-LMBP Hybrid Neural Network Algorithm in Image Denoising

In order to overcome the disadvantages of poor global search ability, slow convergence speed and easy to fall into local minimum in the traditional BP neural network in image denoising, a hybrid ACO-LMBP neural network image denoising algorithm based on ant colony algorithm and LMBP algorithm is pro...

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Bibliographic Details
Published inIOP conference series. Materials Science and Engineering Vol. 439; no. 3; pp. 32120 - 32126
Main Authors Wang, Hai jun, Tao, Jin, Neimule, Menke
Format Journal Article
LanguageEnglish
Published IOP Publishing 09.11.2018
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ISSN1757-8981
1757-899X
1757-899X
DOI10.1088/1757-899X/439/3/032120

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Summary:In order to overcome the disadvantages of poor global search ability, slow convergence speed and easy to fall into local minimum in the traditional BP neural network in image denoising, a hybrid ACO-LMBP neural network image denoising algorithm based on ant colony algorithm and LMBP algorithm is proposed. ACO-LMBP hybrid neural network algorithm has both the high speed of LMBP algorithm and the global nature of ACO algorithm. It can improve the problems of BP algorithm model very well. By comparing with the image denoising effect of Wiener filtering, BP, LMBP and PSO-LMBP model, the denoising model using the ACO-LMBP neural network algorithm has better denoising effect.
ISSN:1757-8981
1757-899X
1757-899X
DOI:10.1088/1757-899X/439/3/032120