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Multimedia Tools and Applications

, Volume 78, Issue 24, pp 35401–35418 | Cite as

Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal

  • Serdar EnginoğluEmail author
  • Uğur Erkan
  • Samet Memiş
Article
  • 177 Downloads

Abstract

In this study, we propose a new method, i.e. Adaptive Riesz Mean Filter (ARmF), by operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we compare the results of ARmF, A New Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Based on Pixel Density Filter (BPDF), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision-Based Algorithm (DBA) by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Image Enhancement Factor (IEF), and Visual Information Fidelity (VIF) for 20 traditional test images (Lena, Cameraman, Barbara, Baboon, Peppers, Living Room, Lake, Plane, Hill, Pirate, Boat, House, Bridge, Elaine, Flintstones, Flower, Parrot, Dark-Haired Woman, Blonde Woman, and Einstein), 40 test images in the TESTIMAGES Database, and 200 RGB test images from the UC Berkeley Dataset ranging in noise density from 10% to 90%. Moreover, we compare the running time of these algorithms. These results show that ARmF outperforms the methods mentioned above. We finally discuss the need for further research.

Keywords

Salt-and-pepper noise Non-linear functions Noise removal Matrix algebra Image denoising Riesz mean 

Notes

Acknowledgements

This work was supported by the Office of Scientific Research Projects Coordination at Çanakkale Onsekiz Mart University, Grant number: FHD-2018-1409.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mathematics, Faculty of Arts and SciencesÇanakkale Onsekiz Mart UniversityÇanakkaleTurkey
  2. 2.Department of Computer Engineering, Faculty of EngineeringKaramanoğlu Mehmetbey UniversityKaramanTurkey

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