A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image

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• 作者：Payman Moallem ; Monire Masoumzadeh ; Mehdi Habibi
• 关键词：Periodic noise ; Gaussian notch filter ; Segmentation ; Region growing
• 刊名：Signal, Image and Video Processing
• 出版年：2015
• 出版时间：July 2015
• 年：2015
• 卷：9
• 期：5
• 页码：1179-1191
• 全文大小：3,315 KB
• 参考文献：1.Smolka, B.: Nonlinear Techniques of Noise Reduction in Digital Color Images. Silesian University Press, Poland (2004)
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• 作者单位：Payman Moallem (1)
  Monire Masoumzadeh (1)
  Mehdi Habibi (1)
  
  1. Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Hezarjerib Street, 81746聽, Isfahan, Iran
  
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711

文摘

Digital images can suffer from periodic noise, resulting in the appearance of repetitive patterns on the image data and quality degradation. In order to effectively reduce the periodic noise effects, a novel adaptive Gaussian notch filter is proposed in this paper. In the presented method, the frequency regions that correspond to noise are determined by applying a segmentation algorithm on the spectral band of the noisy image using an adaptive threshold. Then, a region growing algorithm tries to determine the bandwidth of each periodic noise component separately. Subsequently, proper Gaussian notch filters are used to decrease the periodic noises only at the contaminated noise frequencies. The proposed filter and some other well-known filters including the frequency domain mean and median filters and also the traditional Gaussian notch filter are compared to evaluate the effectiveness of the approach. The results in different conditions show that the proposed filter gains higher performance both visually and quantitatively with lower computational cost. Furthermore, compared with the other methods, the proposed filter does not need any tuning and parameter adjustments.