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Knowledge and Information Systems

, Volume 36, Issue 3, pp 557–577 | Cite as

Machine learning and directional switching median-based filter for highly corrupted images

  • Sohail Masood
  • Ayyaz Hussain
  • M. Arfan JaffarEmail author
  • Anwar M. Mirza
  • Tae-Sun Choi
Regular Paper

Abstract

In this paper, two-stage machine learning-based noise detection scheme has been proposed for identification of salt-and- pepper impulse noise which gives excellent detection results for highly corrupted images. In the first stage, a window of size \(3\times 3\) is taken from image and some other features of this window are used as input to neural network. This scheme has distinction of having very low missed detection (MD) and false positives rates. In the second stage, decision tree-based algorithm (J48) is applied on some well-known statistical parameters to generate rules for noise detection. These noise detection methods give promising results for identification of noise from highly corrupted images. A modified version of switching median filter (directional weighted switching median filter) is proposed for noise removal. Performance of noise detector is measured using MD and false alarm FA. Filtering results are compared with state-of-the-art noise removal techniques in terms of peak signal-to-noise ratio and structural similarity index measure. Extensive experiments are performed to show that the proposed technique gives better results than state-of-the-art noise detection and filtering methods.

Keywords

Impulse noise Noise detection Noise removal Intelligent noise detection 

Notes

Acknowledgments

This work (2012-0005542) was supported by Mid-career Researcher Program through NRF grant funded by the MEST and HEC, Islamabad, Pakistan.

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Sohail Masood
    • 1
  • Ayyaz Hussain
    • 2
  • M. Arfan Jaffar
    • 1
    • 4
    Email author
  • Anwar M. Mirza
    • 3
  • Tae-Sun Choi
    • 4
  1. 1.International Islamic University IslamabadIslamabadPakistan
  2. 2.National University of Computer and Emerging SciencesIslamabadPakistan
  3. 3.King Saud UniversityRiyadhSaudi Arabia
  4. 4.Gwangju Institute of Science and TechnologyGwangjuSouth Korea

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