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Complex-Fuzzy Adaptive Image Restoration – An Artificial-Bee-Colony-Based Learning Approach

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Intelligent Information and Database Systems (ACIIDS 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6592))

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Abstract

A complex-fuzzy approach using complex fuzzy sets is proposed in the paper to deal with the problem of adaptive image noise cancelling. A image may be corrupted by noise, resulting in the degradation of valuable image information. Complex fuzzy set (CFS) is in contrast with traditional fuzzy set in membership description. A CFS has the membership state within the complex-valued unit disc of the complex plane. Based on the membership property of CFS, we design a complex neural fuzzy system (CNFS), so that the functional mapping ability by the CNFS can be augmented. A hybrid learning method is devised for training of the proposed CNFS, including the artificial bee colony (ABC) method and the recursive least square estimator (RLSE) algorithm. Two cases for image restoration are used to test the proposed approach. Experimental results are shown with good restoration quality.

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Authors and Affiliations

  1. Laboratory of Intelligent Systems and Applications, Department of Information Management, National Central University, Taiwan, ROC

    Chunshien Li & Fengtse Chan

Authors
  1. Chunshien Li
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  2. Fengtse Chan
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Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Engineering, Yeungnam University, Dae-Dong, 712-749, Gyeungsan, Korea

    Chong-Gun Kim

  3. Institute of Informatics, Automation and Robotics, Wroclaw University of Technology, 50-370, Wrocław, Poland

    Adam Janiak

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© 2011 Springer-Verlag Berlin Heidelberg

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Li, C., Chan, F. (2011). Complex-Fuzzy Adaptive Image Restoration – An Artificial-Bee-Colony-Based Learning Approach. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_10

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  • DOI: https://doi.org/10.1007/978-3-642-20042-7_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20041-0

  • Online ISBN: 978-3-642-20042-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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