A New Fast Algorithm for Training Large Window Stack Filters

  • Guangming Shi
  • Weisheng Dong
  • Li Zhang
  • Jin Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


Stack filters are often employed for suppressing the pulse noise. In general, the larger sizes the stack filters are, the better results are. Unfortunately, available algorithms for designing stack filters can only be suit for small window sizes due to their huge computational complexities. This paper presents a new fast adaptive algorithm for designing a stack filter with large windows. The idea of the new algorithm is to divide a lager window into many sub-windows. The procedures of dividing a large window are given. An Immune Memory Clonal Selection Algorithm is employed to design the stack filters with small window sizes. Because of its highly parallel structure, it can be very fast implemented. As an experiment, the algorithm was used to restore images corrupted by uncorrelated additive noise with the level from 10% to 50 %. The results show that the algorithm is effective and feasible.


Window Size Fast Algorithm Impulse Noise Artificial Immune System Large Window 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guangming Shi
    • 1
  • Weisheng Dong
    • 1
  • Li Zhang
    • 1
  • Jin Pan
    • 2
  1. 1.School of Electronic EngineeringXidian UniversityXi’anChina
  2. 2.Lab of Network Security and CountermeasureXi’an Communications 

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