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Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images

Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images

Xin-Ming Zhang, Qiang Kang, Jin-Feng Cheng, Xia Wang
Copyright: © 2018 |Volume: 11 |Issue: 3 |Pages: 15
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781522543220|DOI: 10.4018/JITR.2018070104
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MLA

Zhang, Xin-Ming, et al. "Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images." JITR vol.11, no.3 2018: pp.47-61. http://doi.org/10.4018/JITR.2018070104

APA

Zhang, X., Kang, Q., Cheng, J., & Wang, X. (2018). Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images. Journal of Information Technology Research (JITR), 11(3), 47-61. http://doi.org/10.4018/JITR.2018070104

Chicago

Zhang, Xin-Ming, et al. "Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images," Journal of Information Technology Research (JITR) 11, no.3: 47-61. http://doi.org/10.4018/JITR.2018070104

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Abstract

In order to accelerate denoising and improve the denoising performance of the current median filters, an Adaptive Four-dot Median Filter (AFMF) for image restoration is proposed in this article. AFMF is not only very efficient and fast in logic execution, but also it can restore the corrupted images with 1–99% densities of salt-and-pepper noise to the satisfactory ones. Without any complicated operation for noise detection, it intuitively and simply distinguishes impulse noises, while keeping the noise-free pixels intact. Only the uncorrupted pixels of the four-dot mask in adaptive filtering windows are used for the adoption of candidates for median finding, whatever filtering window size is. Furthermore, the adoption of recursive median filters leads to denoising performance improvement and faster filtering. The simple logic of the proposed algorithm obtains significant milestones on the fidelity of a restored image. Relevant experimental results on subjective visualization and objective digital measure validate the robustness of the proposed filter.

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