BP Neural Network Fuzzy Image Restoration Basedon Brain Storming Optimization Algorithm

Xiaoping LIANG; Zhenjun GUO; Changhong ZHU

doi:10.11999/JEIT190261

Volume 41 Issue 12

Dec. 2019

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Xiaoping LIANG, Zhenjun GUO, Changhong ZHU. BP Neural Network Fuzzy Image Restoration Basedon Brain Storming Optimization Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2980-2986. doi: 10.11999/JEIT190261

Citation:

Xiaoping LIANG, Zhenjun GUO, Changhong ZHU. BP Neural Network Fuzzy Image Restoration Basedon Brain Storming Optimization Algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2980-2986. doi: 10.11999/JEIT190261

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BP Neural Network Fuzzy Image Restoration Basedon Brain Storming Optimization Algorithm

doi: 10.11999/JEIT190261

Guilin University of Aerospace Technology, Guilin 541004, China

Funds: 2019 Guangxi University Young and Middle-aged Teachers’ Basic Scientific Research Ability Improvement Project (2019KY0802), The Project of Guilin University of Aerospace Technology Electronic Information Key Discipline and Internet Of Things and Big Data Application Research Center (KJPT201805)

Received Date: 2019-04-17
Rev Recd Date: 2019-09-03

Available Online: 2019-09-12

Publish Date: 2019-12-01

Abstract

Abstract

A kind of restoration method of BP neural network fuzzy image based on Optimized Brain Storming intelligent Optimized(OBSO-BP) algorithm is proposed in this paper. With the method of brain storming intelligent optimized algorithm which is optimized in both clustering and variation, issues of multi-peak high-dimensional function is easily solved. This method optimizes brain storming intelligence algorithm from two aspects of clustering and mutation. This method makes use of the characteristics of brain storming optimization algorithm, which is easy to solve multi-peak and high-dimensional function problems, to automatically search for better initial weights and thresholds of BP neural network, thus reducing the sensitivity of BP network to its initial weights and thresholds, avoiding the network falling into local optimal solution, increasing the convergence speed of the network and reducing the network error and improving the quality of image restoration. Twenty different images are adopted to the image restoration experiment of their fuzzy images with Wiener filtering restoration(Wiener), Wiener filtering restoration based on optimized Brain Storming intelligent Optimized algorithm(Wiener-BSO), BP neural network restoration and BP neural network restoration based on optimized Brain Storming intelligent Optimized algorithm(BSO-BP). Results show that a better effect of image restoration can be achieved with this method.
- Image restoration,
- BP neural network,
- Brain storming algorithm

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References(16)

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