Signal, Image and Video Processing

, Volume 9, Issue 5, pp 1179–1191 | Cite as

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

  • Payman MoallemEmail author
  • Monire Masoumzadeh
  • Mehdi Habibi
Original Paper


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.


Periodic noise Gaussian notch filter Segmentation Region growing 

List of symbols


Adaptive Gaussian notch Filter


Gaussian notch Filter


Low frequencies region LFR


Mean absolute error


Standard deviation


Structural similarity


  1. 1.
    Smolka, B.: Nonlinear Techniques of Noise Reduction in Digital Color Images. Silesian University Press, Poland (2004)Google Scholar
  2. 2.
    Drigger, R., Cox, P., Edwards, T.: Introduction to Infrared and Electro-Optical System. Artech House, Norwood (1999) Google Scholar
  3. 3.
    Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing. Academic Press, London (1997)Google Scholar
  4. 4.
    Castelli, V., Bergman, L.D.: Image Databases. Search and Retrieval of Digital Imagery. Wiley, New York (2002)Google Scholar
  5. 5.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using Matlab. Prentice-Hall, New Jersey (2004)Google Scholar
  6. 6.
    Aizenberg, I., Butakoff, C.: Frequency domain median-like filter for periodic and quasi-periodic noise removal. In: SPIE Proceedings of the Image Processing, 4667, pp. 181–191 (2002)Google Scholar
  7. 7.
    Aizenberg, I., Butakoff, C.: Nonlinear frequency domain filter for quasi periodic noise removal. In: Proceeding of International TICSP Workshop on Spectra Methods and Multirate Signal Processing. TICSP Series, 17, pp. 147–153 (2002)Google Scholar
  8. 8.
    Aizenberg, I., Butakoff, C.: A windowed Gaussian notch filter for quasi-periodic noise removal. Image Vis. Comput. 26, 1347–1353 (2008)CrossRefGoogle Scholar
  9. 9.
    Venkateswarlu, R., Sujata, K.V., Venkateswara Rao, B.: Centroid tracker and point selection. In: SPIE Proceedings, 1697, pp. 520–529 (1992)Google Scholar
  10. 10.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, New York (2010)Google Scholar
  11. 11.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Payman Moallem
    • 1
    Email author
  • Monire Masoumzadeh
    • 1
  • Mehdi Habibi
    • 1
  1. 1.Department of Electrical Engineering, Faculty of EngineeringUniversity of IsfahanIsfahanIran

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