MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||5|
|Section||Digital Signal and Image Processing|
|Published online||19 June 2018|
Application of HS-LMBP Hybrid Neural Network Algorithm in Image Denoising
Ordos Institute Of Technology, Inner Mongolia Ordos, China
* Corresponding author:firstname.lastname@example.org
In order to overcome the problems such as poor global search ability, slow convergence rate, and easy to fall into local minimum values in the image denoising process of traditional BP neural networks, the HS-LMBP hybrid neural network image denoising algorithm is proposed which combines the harmony search algorithm and the LMBP algorithm. The HS-LMBP hybrid neural network algorithm combines the high speed of the LMBP algorithm and the global nature of the HS algorithm, which can be a good improvement to the existing problems of the BP algorithm model. Compared with the Wiener filtering, BP, LMBP and PSO-LMBP model image denoising effects, the denoising model using HS-LMBP neural network algorithm has a better denoising effect.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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