Parametric Image Restoration Using Consensus: An Application to Nonstationary Noise Filtering

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• 作者：Luis González-Jaime (19)
  Mike Nachtegeal (19)
  Etienne Kerre (19)
  Gonzalo Vegas-Sánchez-Ferrero (20)
  Santiago Aja-Fernández (20)
  
• 关键词：Nonstationary noise ; OWA operator ; noise filtering
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7887
• 期：1
• 页码：366-373
• 全文大小：872KB
• 参考文献：1. Lim, J.S.: Two-dimensional signal and image processing, vol. 1, 710 p. Prentice-Hall, Englewood Cliffs (1990)
  2. Bustince, H., Barrenechea, E., Calvo, T., James, S., Beliakov, G.: Consensus in multiexpert decision making problems using penalty functions defined over a Cartesian product of lattices. Information Fusion (in press)
  3. Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decision-making. IEEE Transactions on Systems Man and Cybernetics?18(1), 183-90 (1988) CrossRef
  4. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing?13(4), 600-12 (2004) CrossRef
  
• 作者单位：Luis González-Jaime (19)
  Mike Nachtegeal (19)
  Etienne Kerre (19)
  Gonzalo Vegas-Sánchez-Ferrero (20)
  Santiago Aja-Fernández (20)
  
  19. Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 - S9, 9000, Ghent, Belgium
  20. Laboratorio de Procesado de Imagen, ETSI Telecomunicación, Universidad de Valladolid, 47011, Valladolid, Spain
  
• ISSN：1611-3349

文摘

Image quality gets affected by unavoidable degradations. Several techniques have been proposed based on a priori information of the degradation. However, these techniques fail when the underlying parameters cannot be estimated. We propose a method to deal with situations when the underlying parameters are not known. It is based on the consensus achieved by using a set of aggregation functions and a penalty function. The method is tested in the case of a nonstationary Gaussian noise, and the Wiener filter is used to prove this methodology. The results show that the approach is consistent and it achieves comparable results for known parameters.