吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 998-1004.doi: 10.13229/j.cnki.jdxbgxb201503046

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Weighted neighbor-region based multi-level fuzzy edge detection method

ZHANG Wen-jie1, XIONG Qing-yu2, SHI Wei-ren1, CHEN Shu-han1   

  1. 1.College of Automation, Chongqing University, Chongqing 400030, China;
    2.The School of Software Engineering, Chongqing University, Chongqing 401331, China
  • Received:2013-10-23 Online:2015-05-01 Published:2015-05-01

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Abstract: Most existing edge detection methods can not perform well in low contrast image and noise images. Combing the advantages of differential operator and fuzzy edge detection, a novel multi-level fuzzy edge detection method based on weighted neighbor-region is proposed. First, this method computes the image gradient features, and utilizes the adaptive method to divide image into multiple tiers based on gradient. Then, a fuzzy function is constructed to strengthen different image gradient features in different levels. Experiment results show that the proposed approach can highlight the gradient features in low contrast region of an image with the help of strengthening. Also this approach outperforms the state-of-art methods in terms of both visual quality and noise (Salt and Pepper noise and Gaussian noise) suppression.

Key words: information processing, edge detection, weighted neighbor-region, stratified fuzzy enhancement

CLC Number: 

  • TN911.73
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