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Switching impulse noise filter based on Laplacian convolution and pixels grouping for color images

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Abstract

This paper presents a new switching filter consisting of three steps to restore color images corrupted by impulse noise. Firstly, Laplacian convolution is performed on pixels in four directions to mark the pixels which are radically different in value from neighboring pixels as noise candidates. Secondly, those missed neighboring pixels involved in the step of pixels grouping decrease the occurrence of false detection. Pixels in the observation window are separated into noisy pixels and normal pixels with a dividing threshold, whose value is assigned according to a noise density estimator. Finally, a modified arithmetic mean filter is applied to restore the polluted image. Extensive experiments show that the proposed method achieves better performance than comparative methods in terms of peak-signal-to-noise ratio and structural similarity. The proposed method can effectively remove impulse noise in which noise density is varying from 10 to 80%.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61674115), the National Natural Science Foundation of China (No. 61404090) and the National High Technology Research and Development Program of China (863 program, No. 2012AA012705)

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Authors and Affiliations

  1. School of Microelectronics, Tianjin University, No. 92, Weijin Road, Tianjin, 300072, China

    Jing Gao, Zengquan Du, Zaifeng Shi, Zehao Xu & Rui Tang

  2. School of Mathematic Science, Tianjin Normal University, No. 393, Extension of Bin Shui West Road, Tianjin, 300387, China

    Qingjie Cao

  3. Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, No. 92, Weijin Road, Tianjin, 300072, China

    Jing Gao & Zaifeng Shi

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  1. Jing Gao
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  2. Zengquan Du
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  3. Zaifeng Shi
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  4. Zehao Xu
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  5. Qingjie Cao
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  6. Rui Tang
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Correspondence to Zaifeng Shi.

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Gao, J., Du, Z., Shi, Z. et al. Switching impulse noise filter based on Laplacian convolution and pixels grouping for color images. SIViP 12, 1523–1529 (2018). https://doi.org/10.1007/s11760-018-1308-7

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  • DOI: https://doi.org/10.1007/s11760-018-1308-7

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