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Linear cellular automata-based impulse noise identification and filtration of degraded images

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Abstract

In this paper, four impulse noise filters based on modifications of linear cellular automata (LCA) are proposed and evaluated. Each of these filters make use of an adaptive neighborhood to provide efficient noise filtration at varying noise densities. The LCA model works asynchronously and makes the proposed filters computationally efficient. Peak signal to noise ratio and structural similarity (SSIM) index metrics are used to provide objective analysis of the proposed filters.

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Authors and Affiliations

  1. Department of Computer Science, North Campus, University of Kashmir, Srinagar, Jammu and Kashmir, India

    Zubair Jeelani, Gulnawaz Gani & Fasel Qadir

  2. Islamic University of Science and Technology, Kashmir, India

    Zubair Jeelani

Authors
  1. Zubair Jeelani
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  2. Gulnawaz Gani
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  3. Fasel Qadir
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Correspondence to Fasel Qadir.

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Jeelani, Z., Gani, G. & Qadir, F. Linear cellular automata-based impulse noise identification and filtration of degraded images. SIViP 17, 2679–2687 (2023). https://doi.org/10.1007/s11760-023-02484-4

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  • DOI: https://doi.org/10.1007/s11760-023-02484-4

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