Adaptive Switching Median Filter Based on GA-BP Neural Network

Xiao Ling Ye; Yan Yan Dou; Bo Liu

doi:10.4028/www.scientific.net/AMR.753-755.2980

Paper Titles

Grey Incidence Cluster Analysis of Colliery Security Management Evaluating Indicator
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Multi-Features Fusion Algorithm Based on NSCT for Face Recognition
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Photograph Method Used in Computerized Log Centering
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Study on Motion Blur Image Restoration Algorithms
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Adaptive Switching Median Filter Based on GA-BP Neural Network
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Analysis of Iris Images Segmentation Methods Based on Level Set
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Design and Implementation of Dynamic Menu Based on Role-Based Access Control
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Study on Iris Segmentation Method Using Chan-Vese Model
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Research and Application on STIW Classification Method
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755Adaptive Switching Median Filter Based on GA-BP...

Adaptive Switching Median Filter Based on GA-BP Neural Network

Abstract:

For the poor performance of conventional filtering algorithms in removing salt and pepper noise from digital images under high noise density,an adaptive switching median filter algorithm based on BP neural network optimized by genetic algorithm (GA) is proposed to detect and remove salt and pepper noise from images. Firstly,the initial weights and thresholds of BP neural network are optimized by genetic algorithm.Then image pixels are devided into either signal or noise points by the trained network automatically. The detected noise points will be removed by adaptive switching median filter algorithm,but nothing to do with the signal points. Experiment results show that the proposed algorithm significantly outperforms the others and efficiently removes salt and pepper noise from digital images without distorting image details and textures .