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Difference based median filter for removal of random value impulse noise in images

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Abstract

Random value impulse noise of images has many sources, such as image sensor, electronic components, etc. How to removal of noise and restore degraded image is always an interesting problem. The decision based algorithms as efficient methods to suppress noise have been extensively studied for a long time. In this type of algorithms, the first step is to classify the corrupted pixels from the surroundings, but it is not an easy thing since each image is different. The efficiency of the classification has great influence on the overall performances of the algorithms. A difference based median filter which can efficiently locate the random value impulse noise is proposed in this paper. Based on this filter, a new algorithm for removal of impulse noise in images is designed. A comparison of the performances is made among several existing algorithms in term of Image Enhancement Factor, Peak Signal-to-Noise Ratio and Structure Similarity Index. Finally, the proposed method is used for underwater image processing to suppress the random value impulse noise modified by Histogram Equalization operation. Visual and quantitative results indicate that the proposed method outperforms most of algorithms for removal of impulse noise in literatures.

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References

  1. Ao J, Ma C (2018) Adaptive stretching method for underwater image color correction. Int J Pattern Recognit Artif Intell 32(2):1854001

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  3. Dale-Jones R, Tjahjadi T (1993) A study and modification of the local histogram equalization algorithm. Pattern Recogn 26(9):1373–1381

    Article  Google Scholar 

  4. Eng HL, Ma KK (2001) Noise adaptive soft-switching median filter. IEEE Trans Image Process 10:242–251

    Article  Google Scholar 

  5. Gonzalez RC, Woods RE (2008) Digital image processing, Third edn. Pearson Education, Upper Saddle River, pp 343–345

  6. Huang TS, Yang GJ, Tang GY (1979) A Fast two dimensional median filtering algorithm. IEEE Trans Acoust Speech Signal Process 27:13–18

    Article  Google Scholar 

  7. Hwang H, Hadded RA (1995) adaptive median filter: new algorithms and results. IEEE Trans Image Process 4:499–502

    Article  Google Scholar 

  8. Jayaraj V, Ebenezer D (2010) A new switching-based median filtering scheme and algorithm for removal of high-density salt and pepper noise in images. EURASIP Journal on Advances in Signal Processing 1–11

  9. Karthik B, Kumar TVUK (2014) Removal of high density salt and pepper noise through modified cascaded filter. Middle-East J Sci Res 20:1222–1228

    Google Scholar 

  10. Kim YT (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans on Comsumer Electronics 43:1–8

    Article  Google Scholar 

  11. Lee CS, Kuo YH, Yu PT (1997) “Weighted fuzzy mean filters for image processing”, Fuzzy Sets and Systems. Elsevier 89:157–180

    Google Scholar 

  12. Menotti D, Najman L (2007) J. Facon and Arnaldo de A. Araujo, “Multi-Histogram equalization methods for contrast enhancement and brightness preserving”. IEEE Tans On Consumer Electronics 53:1186–1194

    Article  Google Scholar 

  13. Nair MS, Revathy K, Tatavarti R (2008) Removal of salt-and pepper noise in image: a new decision-based algorithm. Proc Of the International MultiConference of Engineers and Computer Scientists, Hong Kong I:611–616

    Google Scholar 

  14. Pizer SM et al (1987) Adaptive histogram equalization and its varantions. Comput Vis Graph Image Process 39:355–368

    Article  Google Scholar 

  15. Stinson DR (2005) Cryptography theory and practice. Third Edition. Chapman & Hall/CRC, ch. 3

  16. Sun F, Zhang X, Wang G (2011) An Approach for Underwater Image Denoising Via Wavelet Decomposition and High-pass Filter. Fourth International Conference on Intelligent Computation Technology and Automation. IEEE Computer Society 417–420

  17. Toh KKV, Isa NAMM (2010) Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Processing Letters 17:281–284

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Wang X, Liu Y, Zhang H et al (2015) A total variation model based on edge adaptive guiding function for remote sensing image de-noising. Int J Appl Earth Obs Geoinf 34(1):89–95

    Article  Google Scholar 

  20. Zhang S, Karim MA (2002) A new impulse detector for switching median filters. IEEE Signal Process Letter 9(11):360–363

    Article  Google Scholar 

  21. Zou R, Shi C (2011) Curvelet-based the VisualShrink Threshold for Interfered Infrared Image Denoising. Energy Procedia 13:4712–4717

    Article  Google Scholar 

  22. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Grapics Gems IV, (Academic Press Professional, Inc., San Diego, pp. 474–485

Download references

Acknowledgments

This work is supported by the program for National Natural Science Foundation of China (No. 61167006) and GUET Excellent Graduate Thesis Program (No. 16YJPYSS13).

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Correspondence to Jun Ao.

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Ma, C., Lv, X. & Ao, J. Difference based median filter for removal of random value impulse noise in images. Multimed Tools Appl 78, 1131–1148 (2019). https://doi.org/10.1007/s11042-018-6442-2

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  • DOI: https://doi.org/10.1007/s11042-018-6442-2

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