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Switching median filter for suppressing multi-pixel impulse noise
A.A. Trubitsyn 1, E.Yu. Grachev 1

Ryazan State Radio Engineering University named after V.F. Utkin,
390005, Ryazan, Gagarina st., 59/1

 PDF, 2646 kB

DOI: 10.18287/2412-6179-CO-841

Страницы: 580-588.

Язык статьи: English

Аннотация:
This paper proposes a new switching median filter for suppressing multi-pixel impulse noise in X-ray images. A multi-pixel impulse is understood as a set of several neighboring pixels, the intensity of each significantly exceeds background intensity. Multi-pixel noise can occur, for example, due to the blooming effect, the reason being the limited value of pixel saturation capacity. This article defines the thresholds for the intensity increment relative to the eight immediate neighbors, above which the current pixel is processed by the median filter. The dependence of these thresholds on the number of pixels in an impulse is presented. The proposed algorithm is based on the median filtering process, which consists of several iterations. In this case, the filter has the smallest possible size, which minimizes image distortion during processing. In particular, to exclude a single-pixel impulse, pixel processing is turned on when intensity surge exceeds 3.5 with the grayscale value ranging from 0 to 1. At the same time, to exclude nine-pixel impulses, three iterations are required with the following thresholds: the first iteration with a threshold 2.0; the second iteration also with a threshold 2.0 and the third iteration with a threshold 3.5. The algorithm proposed was tested on real X-ray images corrupted by multi-pixel impulse noise. The algorithm is not only simple, but also reliable and suitable for real-time implementation and application. The efficiency of the technique is shown in comparison with other known filtering methods with respect to the degree of noise suppression. The main result of the testing is that only the proposed method allows excluding multi-pixel noise. Other advantage of the algorithm is its weak effect on the level of Gaussian noise leading to the absence of image blurring (or preserving image details) during processing.

Ключевые слова:
image processing, digital image processing, X-ray imaging, image enhancement, median filter, impulse noise.

Благодарности
The research has been carried out due to the support of the Russian Science Foundation grant (project No.18-79-10168).

Citation:
Trubitsyn AA, Grachev EY. Switching median filter for suppressing multi-pixel impulse noise. Computer Optics 2021; 45(4): 580-588. DOI: 10.18287/2412-6179-CO-841.

Литература:

  1. Gonzalez RC, Woods RE. Digital image processing. 3th ed. Upper Saddle River, NJ: Prentice Hall; 2007. ISBN: 978-0-13-168728-8.
  2. Grachev E, Trubitsyn A, Kirushin D, Fefelov A. Development of algorithms and software of flat X-ray image processing. Proc 8th Mediterranean Conf on Embedded Computing (MECO’2019) 2019: 8760151. DOI: 10.1109/MECO.2019.8760151.
  3. Wang Z, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans Circuits Syst II 1999; 46(1): 78-80. DOI: 10.1109/82.749102.
  4. Toh KKV, Isa NAM. Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Process Lett 2010; 17(3): 281-284. DOI: 10.1109/LSP.2009.2038769.
  5. Hsieh MH, Cheng FC, Shie MC, Ruan SJ. Fast and efficient median filter for removing 1-99% levels of salt-and-pepper noise in images. Eng Appl Artif Intell 2013; 26(4): 1333-1338. DOI: 10.1016/j.engappai.2012.10.012.
  6. Vijaykumar VR, Vanathi PT, Kanagasabapathy P, Ebenezer D. High density impulse noise removal using robust estimation based filter. IAENG Int J Comput Sci 2008; 35(3): 259-266.
  7. Hwang H, Haddad RA. Adaptive median filters: new algorithms and results. IEEE Trans Image Process 1995; 4(4): 499-502. DOI: 10.1109/83.370679.
  8. Kam, HS, Tan WH. Noise detection fuzzy (NDF) filter for removing salt and pepper noise. In Book: Zaman HB, Robinson P, Petrou M, Olivier P, Schröder H, Shih TK, eds. Visual informatics: Bridging research and practice. Berlin, Heidelberg: Springer-Verlag; 2009: 479-486. DOI: 10.1007/978-3-642-05036-7_45.
  9. Ng PE, Ma KK. A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans Image Process 2006; 15(6): 1506-1516. DOI: 10.1109/TIP.2005.871129.
  10. Ramadan ZM. A new method for impulse noise elimination and edge preservation. Can J Elect Comput Eng 2014; 37(1): 2-10. DOI: 10.1109/CJECE.2014.2309071.
  11. Singh N, Thilagavathy T, Lakshmipriya RT, Umamaheswari O.  Some studies on detection and filtering algorithms for the removal of random valued impulse noise. IET Image Process 2017; 11(11): 953-963. DOI: 10.1049/iet-ipr.2017.0346.
  12. Garnett R, Huegerich T, Chui C, He W. A universal noise removal algorithm with an impulse detector. IEEE Trans Image Process 2005; 14(11): 1747-1754. DOI: 10.1109/TIP.2005.857261.
  13. Dong Y, Chan RH, Xu S. A detection statistic for random-valued impulse noise. IEEE Trans Image Process 2007; 16(4): 1112-1120. DOI: 10.1109/TIP.2006.891348.
  14. Civicioglu P. Removal of random-valued impulsive noise from corrupted images. IEEE Trans Consum Electron 2009; 55(4): 2097-2104. DOI: 10.1109/TCE.2009.5373774.
  15. Lan X, Zuo Z. Random-valued impulse noise removal by the adaptive switching median detector and detail-preserving regularization. Optik 2014; 125(3): 1101-1105. DOI:10.1016/j.ijleo.2013.07.114.
  16. Singh N, Umamaheswari O. A denoising algorithm for random valued impulse noise in images using measure of dispersions. Proc 4th Int Conf on Signal Processing, Communication and Networking (ICSCN-2017) 2017: 14.
  17. Grachev EY, Serebryakov AE, Trubitsyn AA, Goltcev AA, Papenkov MA. The visualization system of microfocus X-ray images with automatic adjustment of zoom and focus. Instrum Exp Tech 2018; 61(2): 268-276. DOI: 10.1134/S0020441218010244.
  18. Fellers TJ, Davidson MW. Hamamatsu. Concepts in digital imaging technology: CCD saturation and blooming. Source: <https://web.archive.org/web/20120727032200/http://learn.hamamatsu.com/articles/ccdsatandblooming.html>.
  19. Grachev E, Kozlov E, Trubitsyn A. Designing X-Ray micro computed tomograph as a mechatronic system. ACM Int Conf Proc Series 2019; F147614: 34-39. DOI: 10.1145/3314493.3314501.
  20. Mafi M, Martin H, Andrian J,  Barreto A, Adjouadi M. A comprehensive survey on impulse and gaussian denoising filters for digital images. Signal Process 2019; 157: 236-260. DOI: 10.1016/j.sigpro.2018.12.006.

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