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A two-stage filter for high density salt and pepper denoising

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Abstract

Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%.

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References

  1. Aiswarya K, Jayaraj V, Ebenezer D (2010) A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos. In Proceedings of IEEE 2nd International Conference on Computer Modeling and Simulation, Sanya.

  2. Bai T, Tan J (2015) Automatic detection and removal of high-density impulse noises. IET Image Process 9(2):162–172

    Google Scholar 

  3. Chan TF, Esedoglu S (2005) Aspects of Total variation regularized L1 function approximation. SIAM J Appl Math 65(5):1817–1837

    MathSciNet  MATH  Google Scholar 

  4. Chan RH, Ho C-W, Nikolova M (2005) Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans Image Process 14(10):1479–1485

    Google Scholar 

  5. Chen T, Wu HR (2001) Adaptive impulse detection using center-weighted median filters. IEEE Sign Proc Lett 8(1):1–3

    Google Scholar 

  6. Enginoglu S, Erkan U, Memis S (2019) Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal. Multimed Tools Appl 78:35401–35418

    Google Scholar 

  7. Erkan U, Gokrem L (2018) A new method based on pixel density in salt and pepper noise removal. Turk J Electr Eng Comput Sci 26:162–171

    Google Scholar 

  8. Erkan U, Gokrem L, Enginoglu S (2018) Different applied median filter in salt and pepper noise. Comput Electr Eng 70:789–798

    Google Scholar 

  9. U Erkan, S Enginoglu, DNH Thanh and LM Hieu (2020) Adaptive frequency median filter for the salt-and-pepper Denoising problem. IET Image Process (In press). https://doi.org/10.1049/iet-ipr.2019.0398

  10. Erkan U, Thanh DNH, Hieu LM, Enginoglu S (2020) An iterative mean filter for image Denoising. IEEE Access 7(1):167847–167859

  11. Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand CH (2011) Removal of high density salt and pepper noise through modified decision based Unsymmetric trimmed median filter. IEEE Sign Proc Lett 18(5):287–290

    Google Scholar 

  12. Goldstein T, Osher S (2009) The split Bregman method for L1 regularized problem. SIAM J Imaging Sci 2(2):323–343

    MathSciNet  MATH  Google Scholar 

  13. Hwang H, Haddad RA (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Proc 4(4):499–502

    Google Scholar 

  14. Lin J-H, Ansari N, Li J (1999) Nonlinear filtering by threshold decomposition. IEEE Trans Image Process 8(7):925–933

    Google Scholar 

  15. Lin C-H, Tsai J-S, Chiu C-T (2010) Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Trans Image Process 19(9):2307–2320

    MathSciNet  MATH  Google Scholar 

  16. Prasath VBS, Vorotnikov D, Pelapur R, Jose S, Seetharaman G, Palaniappan K (2015) Multiscale Tikhonov-Total variation image restoration using spatially varying edge coherence exponent. IEEE Trans Image Process 24(12):5220–5235

    MathSciNet  MATH  Google Scholar 

  17. Prasath VBS, Pelapur R, Seetharaman G, Palaniappan K (2019) Multiscale structure tensor for improved feature extraction and image regularization. IEEE Trans Image Process 28:6198–6210

    MathSciNet  MATH  Google Scholar 

  18. Rojas R, Rodriguez P (2011) Spatially adaptive Total variation image Denoising under salt and pepper noise. 19th European Signal Processing Conference EUSIPCO, Barcelona.

    Google Scholar 

  19. Singh V, Dev R, Dhar NK, Agrawal P, Verma NK (2018) Adaptive type-2 fuzzy approach for filtering salt and pepper noise in grayscale images. IEEE Trans Fuzzy Syst 26(5):3170–3176

    Google Scholar 

  20. Srinivasan KS, Ebenezer D (2007) A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Sign Proc Lett 14(3):189–192

    Google Scholar 

  21. Thanh DNH, Dvoenko S (2016) A method of total variation to remove the mixed Poisson-Gaussian noise. Patt Recog Image Anal 26(2):285–293

    Google Scholar 

  22. Thanh DNH, Prasath VBS and Thanh LT (2018) Total variation L1 Fidelity salt-and-pepper Denoising with adaptive regularization parameter. In Proceedings of IEEE The 5th NAFOSTED Conference on Information and Computer Science (NICS), Ho Chi Minh City.

  23. Thanh DNH, Thanh LT, Hien NN and Prasath VBS (2020) Adaptive total variation L1 regularization for salt and pepper image denoising. Optik (In press). https://doi.org/10.1016/j.ijleo.2019.163677,163677.

  24. Toh KKV, Isa NAM (2010) Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Sign Proc Lett 17(3):281–284

    Google Scholar 

  25. Varghese J, Tairan N, Subash S (2015) Adaptive switching non-local filter for the restoration of salt and pepper impulse-corrupted digital images. Arab J Sci Eng 40(11):3233–3246

    Google Scholar 

  26. Wang Z, Bovik A, Sheikh H, Simoncelli, Eero (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Google Scholar 

  27. Zhao JX, Liu JJ, Fan DP, Cao Y, Yang J, Chen MM (2019) EGNet: edge guidance network for salient object detection. International Conference on Computer Vision (ICCV), Seoul.

    Google Scholar 

  28. Thanh DNH , Dvoenko S, Sang DV (2015) A Denoising Method Based on Total Variation. In Proceedings of the ACM Sixth International Symposium on Information and Communication Technology. https://doi.org/10.1145/2833258.2833281

  29. Thanh DNH, Prasath S, Hieu LM, Dvoenko S (2020) An adaptive method for image restoration based on high-order total variation and inverse gradient. SIViP. https://doi.org/10.1007/s11760-020-01657-9

  30. Hai NH, Thanh DNH, Hien NN, Premachandra C, Prasath VBS (2019) A Fast Denoising Algorithm for X-Ray Images with Variance Stabilizing Transform. In Proceedings of IEEE 2019 11th International Conference on Knowledge and Systems Engineering (KSE), Da Nang

  31. Erkan U, Engınoğlu S, Thanh DNH A Recursive Mean Filter for Image Denoising. In Proceedings of IEEE 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya

  32. Hue NM, Thanh DNH, Thanh LT, Hien NN, Prasath VBS Image Denoising with Overlapping Group Sparsity and Second Order Total Variation Regularization. In Proceedings of IEEE 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi

  33. Hong NM, Thanh NC (2020) Distance-Based Mean Filter for Image Denoising. In Proceedings of ACM the 4th International Conference on Machine Learning and Soft Computing (ICMLSC 2020), Haiphong. https://doi.org/10.1145/3380688.3380704

  34. Kkishorebabu V, Varatharajan R (2020) A decision based unsymmetrical trimmed modified winsorized variants for the removal of high density salt and pepper noise in images and videos. Comput Commun 154:433–441. https://doi.org/10.1016/j.comcom.2020.02.048

  35. Shukla AK, Pandey RK, Reddy PK (2020) Generalized fractional derivative based adaptive algorithm for image denoising. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-08641-y

  36. Karthik B, Kumar TK, Vijayaragavan SP, Sriram M (2020) Removal of high density salt and pepper noise in color image through modified cascaded filter. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01737-1

  37. Thanh DNH, Thanh LT, Prasath VBS, Erkan U (2019) An Improved BPDF Filter for High Density Salt and Pepper Denoising. In Proceedings of 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF), Danang

  38. Chen J, Li F (2019) Denoising convolutional neural network with mask for salt and pepper noise. IET Image Process 13(13):2604–2613

  39. Ahmed F, Das S (2014) Removal of High-Density Salt-and-Pepper Noise in Images With an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean. IEEE Trans Fuzzy Syst 22(5):1352–1358

  40. Chan RH, Ho C-W, Nikolova M (2005) Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans Image Process 14(10):1479–1485

  41. Petrovic NI, Crnojevic V (2008) Universal Impulse Noise Filter Based on Genetic Programming. IEEE Trans Image Process 17(7):1109–1120

  42. Sheela CJJ, Suganthi G (2020) An efficient denoising of impulse noise from MRI using adaptive switching modified decision based unsymmetric trimmed median filter. Biomed Signal Process Control 55:101657. https://doi.org/10.1016/j.bspc.2019.101657

  43. Yahya AA, Tan J, Su B, Hu M, Wang Y, Liu K, Hadi AN (2020) BM3D image denoising algorithm based on an adaptive filtering. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-08815-8

  44. Maggioni M, Monge E, Foi A (2014) Joint removal of random and fixed-pattern noise through spatiotemporal video filtering. IEEE Trans Image Process 23(10):4282–4296

  45. Knausand C, Zwicker M (2014) Progressive image denoising. IEEE Trans Image Process 23(7):3114–3125

  46. Gour N, Khanna P (2019) Speckle denoising in optical coherence tomography images using residual deep convolutional neural network. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-07999-y

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Acknowledgments

This research is funded by University of Economics Ho Chi Minh City, Vietnam.

Author information

Authors and Affiliations

  1. Department of Information Technology, School of Business Information Technology, University of Economics Ho Chi Minh city, Ho Chi Minh City, Vietnam

    Dang N. H. Thanh

  2. Vietnam-Korea University of Information and Communication Technology – The University of Danang, Danang, Vietnam

    Nguyen Hoang Hai

  3. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    V. B. Surya Prasath

  4. Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA

    V. B. Surya Prasath

  5. Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

    V. B. Surya Prasath

  6. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA

    V. B. Surya Prasath

  7. Department of Economics, University of Economics – The University of Danang, Danang, Vietnam

    Le Minh Hieu

  8. Departamento de Engenharia Mecânica, Faculdade de Engenharia, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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  1. Dang N. H. Thanh
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  2. Nguyen Hoang Hai
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  3. V. B. Surya Prasath
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  4. Le Minh Hieu
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  5. João Manuel R. S. Tavares
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Correspondence to Dang N. H. Thanh.

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Thanh, D.N.H., Hai, N.H., Prasath, V.B.S. et al. A two-stage filter for high density salt and pepper denoising. Multimed Tools Appl 79, 21013–21035 (2020). https://doi.org/10.1007/s11042-020-08887-6

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  • DOI: https://doi.org/10.1007/s11042-020-08887-6

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