Abstract
This paper defines an exponentially weighted mean using an exponentially decreasing sequence of simple fractions based on distance. It then proposes a cutting-edge salt-and-pepper noise (SPN) removal filter—i.e., Exponentially Weighted Mean Filter (EWmF). The proposed method incorporates a pre-processing step that detects noisy pixels and calculates threshold values based on the possible noise density. Moreover, to denoise the images operationalizing the calculated threshold values, EWmF employs the exponentially weighted mean (ewmean) in 1-approximate Von Neumann neighbourhoods for low noise densities and k-approximate Moore neighbourhoods for middle or high noise densities. Furthermore, it ultimately removes the residual SPN in the processed images by relying on their SPN densities. The numerical and visual results obtained with MATLAB R2021a manifest that EWmF outperforms nine state-of-the-art SPN filters.
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References
Russ JC, Russ JC (2008) Introduction to image processing and analysis. CRC Press Taylor & Francis Group, New York, USA
Tukey JW (1977) Exploratory data analysis, Reading. Addison-Wesley, MA
Erkan U, Gökrem L, Enginoğlu S (2019) k-Approximate frequency median filter in salt-and-pepper noise. In: 2nd International conference on science and technology; Engineering science and technology. Association of Kutbilge Academicians, Prizren, pp 395–405
Erkan U, Enginoğlu S, Thanh DNH, Hieu LM (2020) Adaptive frequency median filter for the salt and pepper denoising problem. IET Image Process 14:1291–1302
Hwang H, Haddad RA (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Process 4:499–502
Erkan U, Gökrem L, Enginoğlu S (2019) Adaptive right median filter for salt-and-pepper noise removal. Int J Eng Res Dev 11:542–550
Enginoğlu S, Erkan U, Memiş S (2020) Adaptive cesáro mean filter for salt-and-pepper noise removal. El-Cezerî J Sci Eng 7:304–314
Erkan U, Kılıçman A (2016) Two new methods for removing salt-and-pepper noise from digital images. ScienceAsia 42:28–32
Erkan U, Gökrem L (2018) A new method based on pixel density in salt and pepper noise removal. Turkish J Electr Eng Comput Sci 26:162–171
Erkan U, Thanh DNH, Enginoğlu S, Memiş S (2020) Improved adaptive weighted mean filter for salt-and-pepper noise removal. In: 2nd International conference on electrical, communication and computer engineering. IEEE Press, Istanbul, pp 1–5
Erkan U, Enginoğlu S, Thanh DNH (2019) A recursive mean filter for image denoising. In: International artificial intelligence and data processing symposium. IEEE Press, Malatya, pp 1–5
Zhang P, Li F (2014) A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Process Lett 21:1280–1283
Erkan U, Gökrem L, Enginoğlu S (2018) Different applied median filter in salt and pepper noise. Comput Electr Eng 70:789–798
Enginoğlu S, Erkan U, Memiş S (2019) Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal. Multimedia Tools Appl
Erkan U, Thanh DNH, Hieu LM, Enginoğlu S (2019) An iterative mean filter for image denoising. IEEE Access 7:167847–167859
Hosseini H, Hessar F, Marvasti F (2015) Real-time impulse noise suppression from images using an efficient weighted-average filtering. IEEE Signal Process Lett 22:1050–1054
Lu CT, Chen YY, Wang LL, Chang CF (2016) Removal of salt-and-pepper noise in corrupted image using three-values-weighted approach with variable-size window. Pattern Recognit Lett 80:188–199
Kandemir C, Kalyoncu C, Toygar Ö (2015) A weighted mean filter with spatial-bias elimination for impulse noise removal. Digit Sig Process 46:164–174
Satti P, Sharma N, Garg B (2020) Min-max average pooling based filter for impulse noise removal. IEEE Signal Process Lett 27:1475–1479
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612
Asuni N, Giachetti A (2014) TESTIMAGES: a large-scale archive for testing visual devices and basic image processing algorithms. In: Smart tools and apps for graphics. The Eurographics Association, Cagliari, pp 63–70
Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE international conference on computer vision, vol 2. IEEE Press, Vancouver, , pp 416–423
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Enginoğlu, S., Erkan, U., Memiş, S. (2022). Exponentially Weighted Mean Filter for Salt-and-Pepper Noise Removal. In: Dang, N.H.T., Zhang, YD., Tavares, J.M.R.S., Chen, BH. (eds) Artificial Intelligence in Data and Big Data Processing. ICABDE 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 124. Springer, Cham. https://doi.org/10.1007/978-3-030-97610-1_34
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DOI: https://doi.org/10.1007/978-3-030-97610-1_34
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