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


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.


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



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


  1. 1.
    Asuni N, Giachetti A (2014) TESTIMAGES: A Large-Scale Archive for Testing Visual Devices and Basic Image Processing Algorithms. STAG - Smart Tools & Apps for Graphics ConferenceGoogle Scholar
  2. 2.
    Bai T, Tan J, Hu M, Wang Y (2014) A novel algorithm for removal of salt and pepper noise using continued fractions interpolation. Signal Process. CrossRefGoogle Scholar
  3. 3.
    Chen CLP, Liu L, Chen L, Tang YY, Zhou Y (2015) Weighted couple sparse representation with classified regularization for impulse noise removal. IEEE Trans Image Process 24:4014–4026. MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Chen Q-Q, Hung M-H, Zou F (2017) Effective and adaptive algorithm for pepper-and-salt noise removal. IET Image Process 11:709–716. CrossRefGoogle Scholar
  5. 5.
    Chen J, Zhan Y, Cao H, Wu X (2018) Adaptive probability filter for removing salt and pepper noises. IET Image Process. CrossRefGoogle Scholar
  6. 6.
    Deng X, Ma Y, Dong M (2016) A new adaptive filtering method for removing salt and pepper noise based on multilayered PCNN. Pattern Recogn Lett 79:8–17. CrossRefGoogle Scholar
  7. 7.
    Enginoğlu S, Demiriz S (2015) A Comparison With The Convergent, Cesàro Convergent and Riesz Convergent Sequences of Fuzzy Numbers. The 4th International Fuzzy Systems Symposium, 5-6 November 2015 Istanbul-Turkey, pp 416–419Google Scholar
  8. 8.
    Erkan U, Gökrem L (2018) A new method based on pixel density in salt and pepper noise removal. Turkish J Electr Eng Comput Sci. CrossRefGoogle Scholar
  9. 9.
    Erkan U, Kilicman A (2016) Two new methods for removing salt-and-pepper noise from digital images, ScienceAsia. CrossRefGoogle Scholar
  10. 10.
    Erkan U, Gökrem L, Enginoğlu S (2018) Different applied median filter in salt and pepper noise. Comput Electr Eng. CrossRefGoogle Scholar
  11. 11.
    Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand CH (2012) Removal of high density salt & pepper noise through a modified decision based median filter. 2012 Int Conf Informatics, Electron Vision, ICIEV 2012 18:565–570.
  12. 12.
    Filipović M, Jukić A (2014) Restoration of images corrupted by mixed Gaussian-impulse noise by iterative soft-hard thresholding. Eur Signal Process Conf 44:1637–1641. CrossRefGoogle Scholar
  13. 13.
    González-Hidalgo M, Massanet S, Mir A, Ruiz-Aguilera D (2018) Improving salt and pepper noise removal using a fuzzy mathematical morphology-based filter. Appl Soft Comput 63:167–180. CrossRefGoogle Scholar
  14. 14.
    Hwang H, Haddad RA (1995) Adaptive Median Filters: New Algorithms and Results. IEEE Trans Image Process. CrossRefGoogle Scholar
  15. 15.
    Li Y, Chung FL, Wang S (2008) A robust neuro-fuzzy network approach to impulse noise filtering for color images. Appl Soft Comput 8:872–884. CrossRefGoogle Scholar
  16. 16.
    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 Recogn Lett. CrossRefGoogle Scholar
  17. 17.
    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, Proc. 8th Int'l Conf. Computer Vision, 2:416–423.
  18. 18.
    Nair MS, Raju G (2011) Additive noise removal using a novel fuzzy-based filter. Comput Electr Eng 37:644–655. CrossRefGoogle Scholar
  19. 19.
    Nie L, Wang M, Zha Z-J, Chua T-S (2012) Oracle in image search. ACM Trans Inf Syst. CrossRefGoogle Scholar
  20. 20.
    Pratt WK (1975) Semiannual Technical Report. Image Processing Institute, University of Southern CaliforniaGoogle Scholar
  21. 21.
    Roy A, Singha J, Devi SS, Laskar RH (2016) Impulse noise removal using SVM classification based fuzzy filter from gray scale images. Signal Process 128:262–273. CrossRefGoogle Scholar
  22. 22.
    Russ JC, Russ JC (2008) Introduction to image processing and analysis. CRC Press Taylor & Francis Group, Boca Raton, London, New YorkzbMATHGoogle Scholar
  23. 23.
    Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process. CrossRefGoogle Scholar
  24. 24.
    Srinivasan KS, Ebenezer D (2007) A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Process Lett 14:189–192. CrossRefGoogle Scholar
  25. 25.
    Tang Z, Yang Z, Liu K, Pei Z (2016) A new adaptive weighted mean filter for removing high density impulse noise. 21:1003353.
  26. 26.
    Toh KKV, Isa NAM (2010) Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction. 17:281–284CrossRefGoogle Scholar
  27. 27.
    Tukey JW (1977) Exploratory data analysis. Addison­Wesley, Reading, MAzbMATHGoogle Scholar
  28. 28.
    Varatharajan R, Vasanth K, Gunasekaran M, Priyan M, Gao XZ (2017) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput Electr Eng 0:1–15. CrossRefGoogle Scholar
  29. 29.
    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. CrossRefGoogle Scholar
  30. 30.
    Wang G, Li D, Pan W, Zang Z (2010) Modified switching median filter for impulse noise removal. Signal Process 90:3213–3218. CrossRefzbMATHGoogle Scholar
  31. 31.
    Wang S, Liu Q, Xia Y, Dong P, Luo J, Huang Q, Feng DD (2013) Dictionary learning based impulse noise removal via L1-L1 minimization. Signal Process 93:2696–2708. CrossRefGoogle Scholar
  32. 32.
    Wang Y, Wang J, Song X, Han L (2016) An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal. IEEE Signal Process Lett 23:1582–1586. CrossRefGoogle Scholar
  33. 33.
    Zhang Z, Han D, Dezert J, Yang Y (2018) A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning. Signal Process 147:173–189. CrossRefGoogle Scholar

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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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