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A two-stage switching vector median filter based on quaternion for removing impulse noise in color images

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Abstract

This paper presents a novel two-stage filtering algorithm for removing impulse noise in color images. Quaternion theory is used to represent the intensity and chromaticity differences of two color pixels. Use of quaternion treats color pixels as vectors and processes color images as single unit rather than as separated color components. This preserves the existing correlation and three dimensional vector natures of the color channels. In the first stage of noise detection, the color pixels are sorted and assigned a rank based on the aggregated sum of color pixel differences with other pixels inside the filtering window. The central pixel is considered as probably corrupted by an impulse if its rank is bigger than a predefined rank. In the second stage, the probably corrupted candidate is again checked for an edge or an impulse by using four Laplacian convolution kernels. If the minimum difference of these four convolution is larger than a predefined threshold, then the central pixel is regarded as an impulse. For filtering, we extend the size of the sliding window to cover more pixels information. The noisy pixel is replaced by output of weighted vector median filter implemented using the quaternion distance. More weight is assigned to those pixels belonging to the direction of minimum difference. Experimental results indicate the improved performance of the proposed filter in suppressing the impulse noise while retaining the original image details comparing against other well-known filters.

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References

  1. Alexey R, Vitaly K (2017) Impulsive noise removal from color images with morphological filtering. Proc 6th Int Conf Anal Images Social Netw Texts (AIST), Moscow, Russia: 280–291,Springer-Verlag, Berlin

  2. Astola J, Haavisto P, Neuvo Y (1990) Vector median filters. Proc IEEE 78:678–689

    Article  Google Scholar 

  3. Cai C, Mitra SK (2000) A normalized color difference edge detector based on quaternion representation. Proc 2000 Int Conf Image Process Vancover, BC, Canada 2:816–819

    Google Scholar 

  4. Celebi ME, Kingravi HA, Aslandogan YA (2007) Nonlinear vector filtering for impulsive noise removal from color images. J Electron Imag 16(3):033008

    Article  Google Scholar 

  5. El Mehdi C, Rachid A, Hassane B, Wissam J (2017) High density salt and pepper noise suppression using adaptive dual threshold decision based algorithm in fingerprint images. Proc 2017 Intell Syst Comput Vision (ISCV), Fez, Morocco

  6. Evans CJ, Swagwine SJ, Ell TA (2000) Hypercomplexcolor-sensitive smoothing filters. Proc IEEE ICIP 1:541–544

    Google Scholar 

  7. Geng X, Hu X, Xiao J (2012) Quaternion switching filter for impulse noise reduction in color image. Signal Process 92(1):150–162

    Article  Google Scholar 

  8. Hongqing L, Ruibo Z, Yi Z, Xiaorong J, Trieu-Kien T (2017) Speech Denoising using transform domains in the presence of impulsive and Gaussian noises. IEEE Access 5:21193–21203

    Article  Google Scholar 

  9. Jia YB (2015) Quaternions and rotations. Univ Southern California, Los Angeles, Com S 477.57

  10. Jin L, Li D (2007) An efficient color impulse detector and its application to color images. IEEE Signal Process Lett 14(6):397–400

    Article  Google Scholar 

  11. Jin L., Liu H., Xu X. and Song E., (2010) Quaternion-based color image filtering for impulse noise supression. Electron Imag 19(4)

  12. Karakos DG, Tranias PE (1995) Combining vector median and vector directional filters: the directional distance filters. Proc IEEE Int Conf Image Process 95:171–174

    Article  Google Scholar 

  13. Lazhar K, Faouzi C, Moncef G (1999) High –resolution digital resampling using vector rational filters. Opt Eng 38(5)

  14. Lilong S, Brian F (2007) Quaternion color texture segmentation. Comput Vis Image Underst 107:88–96

    Article  Google Scholar 

  15. Lukac R (2003) Adaptive vector median filtering. Pattern Recogn Lett 24(12):1889–1899

    Article  Google Scholar 

  16. Lukac R (2004) Adaptive color image filtering based on center-weighted vector directional filters. Multidim Syst Sign Process 15(2):169–196

    Article  MATH  Google Scholar 

  17. Lukac R, Smolka B (2003) Application of adaptive center-weighted vector median framework for the enhancement of cdna microarray images. Int J Appl Math Comput Sci 13(3):369–383

    MathSciNet  MATH  Google Scholar 

  18. Lukac R, Smolka B, Plataniotis KN, Venetsanopoulos AN (2003) Entropy vector median filter. Proc 1st Iberain Conf Patt Recogn Image Anal (IbPRIA) Lect Notes Comput Sci 2652:1117–1125

    MATH  Google Scholar 

  19. Lukac R, Smolka B, Plataniotis KN, Venetsanopoulos AN (2006) Vector sigma filters for noise detection and removal in color images. J Visual Image Represent 17(1):1–26

    Article  Google Scholar 

  20. Marium A, Hassan D, Hussain D (2018) Texture-oriented image de-noising technique for the removal of random-valued impulse noise. J Electron Imag 27:123–138

    Google Scholar 

  21. Muwei J, Kin-Man L, Junyu D, Linlin S (2015) Visual-patch-attention-aware saliency detection. IEEE Trans Cybernet 45(8):1575–1586

    Article  Google Scholar 

  22. Muwei J, Qiang Q, Junyu D, Xin S, Yujuan S, Kin-Man L (2018) Saliency detection using quaternionic distance based weber local descriptor and level priors. Multimed Tools Applic 77(11):14343–14360

    Article  Google Scholar 

  23. Muwei J, Qiang Q, Junyu D, Yilong Y, Kin-Man L (2018) Integrating QDWD with pattern distinctness and localcontrast for underwater saliency detection. J Vis Commun Image Represent 53:31–41

    Article  Google Scholar 

  24. Palacios-Enriquez A, Panomaryov V, Reyes-Reyes R, Sadovnychiv S (2018) Sparse technique for images corrupted by mixed Gaussian-impulsive noise. Circ Syst Signal Process 35:1–28

    MathSciNet  Google Scholar 

  25. Peng C, Yue R, Sven N, Zhiqiang H, Alexander JD (2017) Joint Channel estimation and impulsive noise mitigation in underwater acoustic OFDM communication systems. IEEE Trans Wirel Commun 16:6165–6178

    Article  Google Scholar 

  26. Perlman S, Eisenhandler S, Lyons P, Shumila M (1987) Adaptive median filtering for impulse noise elimination in real-time TV signals. IEEE Trans Commun 35:646–652

    Article  Google Scholar 

  27. Plataniotis KN, Androutsos D, Venetsanopoulos AN (1996) Adaptive fuzzy filters for multichannel image processing. Signal Process 55(1):93–106

    Article  MATH  Google Scholar 

  28. Roji C, Manglem S (2016) Vector median filters: a survey. Int J Comput Sci Netw Sec 16:66–84

    Google Scholar 

  29. Smolka B (2010) Peer group switching filter for impulse noise reduction in color images. Pattern Recogn Lett. 31:484–495

    Article  Google Scholar 

  30. Smolka B, Malik K, Malik D (2015) Adaptive rank weighted switching filter for impulsive noise removal in color images. J Real-Time Image Proc 10(2):289–311

    Article  Google Scholar 

  31. Subakan ON, Vemuri BC (2011) A quaternion framework for color image smoothing and segmentation. Int J Comput Vis 91(3):233–250

    Article  MathSciNet  MATH  Google Scholar 

  32. Swagwine SJ, Ell TA (2000) Colour image filters based on hypercomplex convolution. IEE Proc Vis Image Signal Process 147(2):89–93

    Article  Google Scholar 

  33. Syamala JP, Pradeep K (2014) A fast algorithm for salt and pepper noise removal with edge preservation using cardinal spline interpolation for intrinsic finger print forensic image. ICT and Critical Infrastructure: Proc 48th Ann Convent Comput Soc India-Vol. II, Advances in Intelligent Systems and Computing 249, Springer, Cham

  34. Tong B, Chao X, Rong Z, Anas F, Lajos H (2017) Joint noise estimation and data detection conceived for LDPC-coded DMT-based DSL systems. IEEE Access 5:23133–23145

    Article  Google Scholar 

  35. Trahanias PE, Venetsanopoulos AN (1993) Vector directional filters: a new class of multichannel image processing filters. IEEE Trans Image Process 2:528–534

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Wang G, Liu Y, Zhao T (2014) A quaternion-based switching filter for colour image denoising. Signal Process 102:216–225

    Article  Google Scholar 

  38. Yasushi A, Takashi M, Yusuke K, Yasuki Y, Hiroaki Y (2018) A study on impulse noise reduction using CNN learned by divided images. Proc 6th IIAE Int Conf Industr Applic Eng 2018, Okinawa, Japan

  39. Youngjin Y, SeongDeok L, Wonhee C, Chang-Yong K (2007) CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones. Proc SPIE-Int Soc Optic Eng, San Jose, CA, United States

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Correspondence to P.Roji Chanu.

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Chanu, P., Singh, K.M. A two-stage switching vector median filter based on quaternion for removing impulse noise in color images. Multimed Tools Appl 78, 15375–15401 (2019). https://doi.org/10.1007/s11042-018-6925-1

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