Skip to main content
SpringerLink
Log in

An advanced computing in fuzzy rule-based preprocessing design of image filters’ system for removing impulse noises

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In this study, advanced computing in fuzzy rule-based preprocessing classifier is used to categorize five distribution types that are identified as noise by noise detection from the service of next era cloud-empowered computing and techniques. The proposed method extracts useful local information from the corrupted image that is supported by filter processing and resulted in more image details to preserve. The fuzzy rule-based preprocessing classifier goes through a four-phase detection procedure to determine the condition of central pixels for the local image window by using the similarity between the neighboring pixels. A filters’ system is set up with our proposed fuzzy rule-based preprocessing classifier with several effective filters to verify its performance. Simulation results are compared with other individual filters by objective numerical measurements and subjectively visual inspection to indicate that our proposed method performs significantly better in terms of noise suppression and detail preservation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Jiang F, Chen BW, Rho S, Ji W, Pan L, Guo H, Zhao D (2016) Optimal filter based on scale-invariance generation of natural images. J Supercomput 72(1):5–23. doi:10.1007/s11227-015-1398-8

    Article  Google Scholar 

  2. Seo S, Kang D (2016) Study on predicting sentiment from images using categorical and sentimental keyword-based image retrieval. J Supercomput 72(9):3478–3488. doi:10.1007/s11227-015-1510-0

    Article  Google Scholar 

  3. Shehab M, Al-Ayyoub M, Jararweh Y, Jarrah M (2016) Accelerating compute-intensive image segmentation algorithms using GPUs. J Supercomput. doi:10.1007/s11227-016-1897-2

    Google Scholar 

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

    Article  Google Scholar 

  5. Jafar IF, AlNa’mneh RA, Darabkh KA (2013) Efficient improvements on the BDND filtering algorithm for the removal of high-density impulse noise. IEEE Trans Image Process 22(3):1223–1232

    Article  MathSciNet  Google Scholar 

  6. Ng PE, Ma KK (2006) A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans Image Process 15(6):1506–1516

    Article  Google Scholar 

  7. Jayaraj V, Ebenezer D (2010) A new switching-based median filtering scheme and algorithm for removal of high-density salt and pepper noise in image. EURASIP J Adv Signal Process 1:1–11

    Google Scholar 

  8. Kalavathy S, Suresh RM (2011) A switching weighted adaptive median filter for impulse noise removal. Int J Comput Appl 28(9):8–13

    Google Scholar 

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

    Article  Google Scholar 

  10. 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 Signal Proc Let 18(5):287–290

    Article  Google Scholar 

  11. Li Z, Liu G, Xu Y, Cheng Y (2014) Modified directional weighted filter for removal of salt & pepper noise. Pattern Recognit Lett 40(15):113–120

    Article  Google Scholar 

  12. Lu CT, Chou TC (2012) Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter. Pattern Recognit Lett 33(10):1287–1295

    Article  Google Scholar 

  13. Xuming Z, Youlun X (2009) Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter. IEEE Signal Proc Let 16(4):295–298

    Article  Google Scholar 

  14. Chen PY, Lien CY (2008) An efficient edge-preserving algorithm for removal of salt-and-pepper noise. IEEE Signal Proc Let 15:833–836

    Article  Google Scholar 

  15. Lien CY, Huang CC, Chen PY, Lin YF (2013) An efficient denoising architecture for removal of impulse noise in images. IEEE Trans Comput 62(4):631–643

    Article  MathSciNet  Google Scholar 

  16. Duan F, Zhang YJ (2010) A highly effective impulse noise detection algorithm for switching median filters. IEEE Signal Proc Let 17(7):647–650

    Article  Google Scholar 

  17. Chou H-H, Lin H-W, Chang J-R (2014) A sparsity-ranking edge-preservation filter for removal of high-density impulse noises. AEU Int J Electron Commun 68(11):1129–1135

    Article  Google Scholar 

  18. Khaire PA, Thakur NV (2012) Image edge detection based on soft computing approach. Int J Comput Appl 51(8):12–14

    Google Scholar 

  19. Salman N (2006) Image segmentation based on watershed and edge detection techniques. Int Arab J Inform Tech 3(2):104–110

    MathSciNet  Google Scholar 

  20. Chen CM, Lu HHS, Chen YL (2003) A discrete region competition approach incorporating weak edge enhancement for ultrasound image segmentation. Pattern Recognit Lett 24(4–5):693–704

    Article  Google Scholar 

  21. Schulte S, Nachtegael M, De Witte V, Van der Weken D, Kerre EE (2006) A fuzzy impulse noise detection and reduction method. IEEE Trans Image Process 15(5):1153–1162

    Article  Google Scholar 

  22. Barile M, Fichten CS, Asuncion JV (2012) Enhancing human rights: computer and information technologies with access for all. Int J Social Humani Comput 1(4):396–407

    Article  Google Scholar 

  23. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Wang Z, Zhang D (1999) Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans Circuits Syst II Analog Digit Signal Process 46(1):78–80

    Article  MathSciNet  Google Scholar 

  26. Tripathi AK, Ghanekar U, Mukhopadhyay S (2011) Switching median filter: advanced boundary discriminative noise detection algorithm. IET Image Process 5(7):598–610

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Editor-in-Chief and Managing Editor of The Journal of Supercomputing and the anonymous Referees for their useful comments and suggestions, which were helpful in improving the presentation and quality of this paper. The author cordially thanks the Ministry of Science and Technology of the Republic of China, Taiwan, for partially financially supporting this research under Contract No. MOST 105-2410-H-146-002.

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, National Ilan University, Yilan, Taiwan, ROC

    Jieh-Ren Chang, Hong-Wun Lin & Hwai-Tsu Hu

  2. Department of Information Management, Hwa Hsia University of Technology, New Taipei, Taiwan, ROC

    You-Shyang Chen

Authors
  1. Jieh-Ren Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. You-Shyang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong-Wun Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hwai-Tsu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to You-Shyang Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, JR., Chen, YS., Lin, HW. et al. An advanced computing in fuzzy rule-based preprocessing design of image filters’ system for removing impulse noises. J Supercomput 73, 3212–3228 (2017). https://doi.org/10.1007/s11227-017-1979-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-017-1979-9

Keywords

Search

Navigation