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Abstract

Image filtering is able to enhance (or otherwise modify, warp, and mutilate) images and create a new image as a result of processing the pixels of an existing image. Each of pixels in the output image is computed as a function of one or several pixels in the input image, usually located near the output pixel. Different kinds of functions produce different results, and are usually used to remove different noise.

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Reference

  1. Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River, NJ.

    Google Scholar 

  2. Ranganath HS, Kuntimad G, Johnson JL (1995) Pulse coupled neural networks for image processing. In: Proceedings of IEEE Southeast Conference, Raleigh, 26–29 March 1995

    Google Scholar 

  3. Zhan K, Zhang HJ, Ma YD (2009) New spiking cortical model for invariant texture retrieval. IEEE Transactions on Neural Networks 20(12): 1980–1986

    Article  MathSciNet  Google Scholar 

  4. Ma YD, Shi F, Li L (2003) A new kind of impulse noise filter based on PCNN. In: Proceedings of 2003 International Conference on Neural Networks and Signal Processing, Nanjing, 14–17 December 2003

    Google Scholar 

  5. Ma YD, Zhang HJ (2008) New image denoising algorithm combined PCNN with gray-scale morphology. Journal of Beijing University of Posts and Telecommunications 31(2): 108–112

    Google Scholar 

  6. Ma YD, Zhang HJ (2007) A novel image de-noising algorithm combined ICM with morphology. In: Proceedings of the 7th International Symposium on Communications and Information Technologies, Sydney, 17–19 October 2007

    Google Scholar 

  7. Ma YD, Shi F, Li L (2003) Gaussian noise filter based on PCNN. In: Proceedings of 2003 International Conference on Neural Networks and Signal Processing, Nanjing, 14–17 December 2003

    Google Scholar 

  8. Ma YD, Lin DM, Zhang BD et al (2007) A novel algorithm of image Gaussian noise filtering based on PCNN time matrix. In: Proceedings of IEEE International Conference on Signal Processing and Communication, Dubai, 24–27 November 2007

    Google Scholar 

  9. Lzhikevich EM (1998) Theoretical foundations of pulse-coupled models. In: Proceedings of the 1998 IEEE International Joint Conference on Neural Networks Part 3: IEEE World Congress on Computational Intelligence, Anchorage, 4–9 May 1998

    Google Scholar 

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© 2010 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg

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Ma, Y., Zhan, K., Wang, Z. (2010). Image Filtering. In: Applications of Pulse-Coupled Neural Networks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13745-7_2

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  • DOI: https://doi.org/10.1007/978-3-642-13745-7_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13744-0

  • Online ISBN: 978-3-642-13745-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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