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Reconstructing images corrupted by noise based on D–S evidence theory

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Abstract

In this paper, a new algorithm of noise reduction for image based on evidence theory is proposed. The values of all pixels are restricted in interval [0, 1], and set of data in each column is a term of mass function, which can be calculated by D–S composition rule. Judging noise can be achieved by comparing with the value of pixel in middle and of the current one. The noise will be removed by substituting the current value with value computed. An improved accelerated algorithm is also presented by sample window of 2 × 2. As a measure of conflict K with greater value shows that there would be noises within the current sample window. At last, Experiment image “Lena” with additive noise shows as a test sample, that better result can be achieved with the algorithm.

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References

  1. Sun T, Ding S, Chen W (2014) Reduced-reference image quality assessment through SIFT intensity ratio. Int J Mach Learn Cybernet 5(6):923–931

    Article  Google Scholar 

  2. Deschrijver G, Kerre EE (2003) On the relationship between some extensions of fuzzy set theory. Fuzzy Sets Syst 133(2):227–235 Cover image

    Article  MathSciNet  MATH  Google Scholar 

  3. De A, Guo C (2014) An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing. Int J Mach Learn Cybernet 5(4):543–551

    Article  Google Scholar 

  4. Oberkampf WL, DeLand SM, Rutherford BM, Diegert KV, Alvin KFA (1999) New methodology for the estimation of total uncertainty in computational simulation. Conference: AIAA Forum on Non-Deterministic Approaches, 1999

  5. Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1(1):3–28

    Article  MathSciNet  MATH  Google Scholar 

  6. Liu Wang-jin (1982) Fuzzy invariant subgroups and fuzzy ideals. Fuzzy Sets Syst 8(2):133–139

    Article  MathSciNet  MATH  Google Scholar 

  7. Rodriguez RM, Martinez L, Herrera F (2012) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119

    Article  Google Scholar 

  8. Xia M, Xu Z, Liao H (2013) Preference relations based on intuitionistic multiplicative information. IEEE Trans Fuzzy Syst 21(1):113–133

    Article  Google Scholar 

  9. Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vision 109(3):209–232

    Article  MathSciNet  MATH  Google Scholar 

  10. Guo G, Chen S, Chen F (2012) Soft subspace clustering with an improved feature weight self-adjustment mechanism. Int J Mach Learn Cybern 3(1):39–49

    Article  Google Scholar 

  11. Ruthven I, Lalmas M (2002) Using Dempster–Shafer’s theory of evidence to combine aspects of information use. J Intell Inf Syst 19(3):267–301

    Article  Google Scholar 

  12. Jones L, Beynon MJ, Holta CA, Roy S (2006) An application of the Dempster–Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery. J Biomech 39(13):2512–2520

    Article  Google Scholar 

  13. Dempster AP (1967) Upper and lower probabilities induced by a multi-valued mapping. Ann Math Stat 38(4):325–339

    Article  MATH  Google Scholar 

  14. Yager RR (2009) Comparing approximate reasoning and probabilistic reasoning using the Dempster–Shafer framework. Int J Approx Reason 50(5):812–821

    Article  MathSciNet  MATH  Google Scholar 

  15. Basir O, Yuan XH (2007) Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Inf Fus 8(4):379–386

    Article  Google Scholar 

  16. Lin TC (2008) Partition belief median filter based on Dempster–Shafer theory for image processing. Pattern Recogn 41(1):139–151

    Article  MATH  Google Scholar 

  17. Hua ZS, Gong BG, Xu XY (2008) A DS-AHP approach for multi-attribute decision making problem with incomplete information. Expert Syst Appl 34(3):2221–2227

    Article  Google Scholar 

  18. Liu Ming, Zhang Fan, Datseris Philip, Huang H (2014) Improving finite state impedance control of active-transfemoral prosthesis using Dempster–Shafer based state transition rules. J Intell Rob Syst 76(3–4):461–474

    Article  Google Scholar 

  19. Ma W, Jiao L, Gong M, Li C (2014) Image change detection based on an improved rough fuzzy c-means clustering algorithm. Int J Mach Learn Cybern 5(3):369–377

    Article  Google Scholar 

  20. Zadeh LA (2014) A note on modal logic and possibility theory. Inf Sci 279(20):908–913

    Article  MathSciNet  MATH  Google Scholar 

  21. Guo K, Li W (2011) Combination rule of D–S evidence theory based on the strategy of cross merging between evidences. Expert Syst Appl 38(10):13360–13366

    Article  Google Scholar 

  22. Bloch I (1996) Some aspect of Dempster–Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recogn Lett 17(8):905–919

    Article  Google Scholar 

  23. Boston JR (2000) A signal detection systembased on Dempster–Shafer theory and comparison to fuzzy detection. IEEE Trans Syst Man Cybern Part C 30(1):45–51

    Article  Google Scholar 

  24. Lin TC (2011) Decision-based fuzzy image restoration for noise reduction based on evidence theory. Expert Syst Appl 38(7):8303–8310

    Article  Google Scholar 

  25. Corpetti T, Planchon O (2011) Cover image front detection on satellite images based on wavelet and evidence theory: application to the sea breeze fronts. Remote Sens Environ 115(2):306–324

    Article  Google Scholar 

  26. Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

    Article  MathSciNet  MATH  Google Scholar 

  27. Smets P (1990) The combination of evidence in the transferable belief model. IEEE Trans Pattern Anal Mach Intell 5(12):447–458

    Article  Google Scholar 

Download references

Acknowledgments

This paper is supported by the National Natural Science Foundation of China (Grant No. 61300121, 51274144, 61170107,), by Natural Science Foundation of Hebei Province (No. A2014210140, A2013208175), by Training Program for Leading Talents of Innovation Teams in the Universities of Hebei Province (LJRC022).

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Correspondence to Ye Zhao.

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Zhao, Y., Mi, Js., Liu, X. et al. Reconstructing images corrupted by noise based on D–S evidence theory. Int. J. Mach. Learn. & Cyber. 8, 611–618 (2017). https://doi.org/10.1007/s13042-015-0353-6

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  • DOI: https://doi.org/10.1007/s13042-015-0353-6

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