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Efficient Non-linear Filter for Impulse Noise Removal in Document Images

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Book cover Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7665))

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Abstract

A novel method is proposed in this paper to restore document images. The proposed method is based on finding the degree of similarity between the tested pixel and its neighbors in different door’s sizes. If the tested pixel in every door size has enough similarity with at least two pixels, then the tested pixel is deemed original pixel. The number of two pixels is chosen, to make sure that the tested pixel is a part of an original text or a part in a series of similar pixels. Simulation results indicate that the new method delivers superior performance rapidly and efficiently either in terms of the noise removal or details preservation.

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Information Technology, AL-Azhar university, Gaza, Palestine

    Ali Awad

Authors
  1. Ali Awad
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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Awad, A. (2012). Efficient Non-linear Filter for Impulse Noise Removal in Document Images. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_2

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  • DOI: https://doi.org/10.1007/978-3-642-34487-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34486-2

  • Online ISBN: 978-3-642-34487-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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