Automatic detection and removal of high-density impulse noises

Tian Bai; Jieqing Tan

Automatic detection and removal of high-density impulse noises

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Author(s): Tian Bai ^{1, 2} and Jieqing Tan ¹
- Affiliations: 1: School of Computer and Information, Hefei University of Technology, Hefei City, People's Republic of China;
  2: School of Software Engineering, University of Science and Technology of China, Hefei City, People's Republic of China
Source: Volume 9, Issue 2, February 2015, p. 162 – 172
DOI: 10.1049/iet-ipr.2014.0286 , Print ISSN 1751-9659, Online ISSN 1751-9667

Received 11/11/2013, Accepted 24/07/2014, Revised 08/07/2014, Published 27/08/2014

This study presents a novel method for automatic detection and removal of high-density impulse noises. The method consists of two parts: the impulse detection part and the impulse noise removal part. In impulse detection part, an automatic detector based on local mean and variance (LMVD) is presented, which can automatically pick out noisy image from massive images and output corrupted grey levels. The detector utilises LMVD of the neighbourhood of corrupted pixels to simulate the cognitive processes of human observing noisy image. In impulse noise removal part, the Newton–Thiele filter (NTF) instead of median filter is applied to remove impulse noise. The process to construct NTF can be divided into two steps: setting up the grid and constructing the Newton–Thiele's rational interpolation on the grid. First, eight adjacent pixels of the corrupted centre pixel are used to construct the two-dimensional grid. If a pixel in the grid is corrupted, a four-direction linear interpolation algorithm will be performed to provide a rough estimate to the corrupted pixel. Second, the corrupted centre pixel value will be updated by Newton–Thiele's rational interpolation on the grid. The NTF has better robustness than existing filters because it does not need to adjust window size or other parameters. Simulations reveal that the proposed detector and filter have perfect performance in terms of both quantitative evaluation and visual quality, especially it can remove the impulse noise effectively even at 90% noise level.

References

1. 1)
  - 26. Tan, J., Fang, Y.: ‘Newton-Thiele's rational interpolants’, Numer. Algorithms, 2000, 24, (1), pp. 141–157 (doi: 10.1023/A:1019193210259).
2. 2)
  - P.E. Ng , K.K. Ma . A Switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans. Image Process. , 6 , 1506 - 1516
3. 3)
  - 17. Van De Ville, D., Nachtegael, M., Van der Weken, D., Kerre, E.E., Philips, W., Lemahieu, I.: ‘Noise reduction by fuzzy image filtering’, IEEE Trans. Fuzzy Syst., 2003, 11, (4), pp. 429–436 (doi: 10.1109/TFUZZ.2003.814830).
4. 4)
  - 4. Hsieh, M.-H., Cheng, F.-C., Shie, M.-C., Ruan, S.-J.: ‘Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images’, Eng. Appl. Artif. Intell., 2013, 26, pp. 1333–1338 (doi: 10.1016/j.engappai.2012.10.012).
5. 5)
  - 12. Esakkirajan, S., Veerakumar, T., Subramanyam, A.N., PremChand, C.H.: ‘Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter’, IEEE Signal Process. Lett., 2011, 18, (5), pp. 287–290 (doi: 10.1109/LSP.2011.2122333).
6. 6)
  - 11. Xu, H., Zhu, G., Peng, H., Wang, D.: ‘Adaptive fuzzy switching filter for images corrupted by impulse noise’, Pattern Recognit. Lett., 2004, 25, (15), pp. 1657–1661 (doi: 10.1016/j.patrec.2004.05.025).
7. 7)
  - 6. Zhou, Y.Y., Ye, Z.F., Huang, J.J.: ‘Improved decision-based detail-preserving variational method for removal of random-valued impulse noise’, IET Image Process., 2012, 6, (7), pp. 976–985 (doi: 10.1049/iet-ipr.2011.0312).
8. 8)
  - 1. Astola, J., Kuosmaneen, P.: ‘Fundamentals of nonlinear digital filtering’ (CRC, Boca Raton, FL, 1997).
9. 9)
  - 20. Majid, A., Lee, C.-H., Mahmood, M.T., Choi, T.-S.: ‘Impulse noise filtering based on noise-free pixels using genetic programming’, Knowl. Inf. Syst., 2012, 32, (3), pp. 505–526 (doi: 10.1007/s10115-011-0456-7).
10. 10)
  - 14. Toh, K.K.V., Isa, N.A.M.: ‘Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction’, IEEE Signal Process. Lett., 2010, 17, (3), pp. 281–284 (doi: 10.1109/LSP.2009.2038769).
11. 11)
  - 11. Aiswarya, K., Jayaraj, V., Ebenezer, D.: ‘A new and efficient algorithm for the removal of high density salt and pepper noise in images and videos’. Second Int. Conf. Computer Modeling and Simulation, 2010, pp. 409–413.
12. 12)
  - 24. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Prentice-Hall, Englewood Cliffs, NJ, 2002).
13. 13)
  - 30. Huo, X., Tan, J.: ‘Bivariate rational interpolant in image inpainting’, J. Inf. Comput. Sci., 2005, 2, (3), pp. 487–492.
14. 14)
  - 13. Toh, K.K.V., Ibrahim, H., Mahyuddin, M.N.: ‘Salt-and-pepper noise detection and reduction using fuzzy switching median filter’, IEEE Trans. Consum. Electron., 2008, 18, (5), pp. 1956–1961 (doi: 10.1109/TCE.2008.4711258).
15. 15)
  - 3. Luo, W.: ‘An efficient detail-preserving approach for removing impulse noise in images’, IEEE Signal Process. Lett., 2006, 13, (7), pp. 413–416 (doi: 10.1109/LSP.2006.873144).
16. 16)
  - K.S. Srinivasan , D. Ebenezer . A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Process. Lett. , 3 , 189 - 192
17. 17)
  - 28. Tan, J.Q.: ‘The limiting case of Thiele's interpolating continued fraction expansion’, J. Comput. Math., 2001, 19, (4), pp. 433–444.
18. 18)
  - R.H. Chan , C.-W. Ho , M. Nikolova . Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Trans. Image Process. , 10 , 1479 - 1485
19. 19)
  - 20. Wang, W., Lu, P.: ‘An efficient switching median filter based on local outlier factor’, IEEE Signal Process. Lett., 2011, 18, (10), pp. 551–554 (doi: 10.1109/LSP.2011.2162583).
20. 20)
  - H.L. Eng , K.K. Ma . Noise adaptive soft-switching median filter. IEEE Trans. Image Process. , 2 , 242 - 251
21. 21)
  - 27. Lorentzen, L., Waadeland, H.: ‘Continued fractions with applications’ (Elsevier Science Publishers, BV, 1992).
22. 22)
  - 5. Chen, T., Wu, H.R.: ‘Application of partition-based median type filters for suppressing noise in images’, IEEE Trans. Image Process., 2001, 10, (6), pp. 829–836 (doi: 10.1109/83.923279).
23. 23)
  - 29. Tan, J.Q., Zhao, Q.J.: ‘Successive Newton-Thiele's rational interpolation’, J. Inf. Comput. Sci., 2005, 2, (2), pp. 295–301.
24. 24)
  - 8. Hsien, H.C., Ling, Y.H., Hwai, T.U.: ‘Turbulent PSO based fuzzy image filter with no reference measures for high density impulse noise’, IEEE Trans. Cybern., 2013, 43, (1), pp. 296–307 (doi: 10.1109/TSMCB.2012.2205678).
25. 25)
  - 16. Nair, M.S., Raju, G.: ‘A new fuzzy-based decision algorithm for high-density impulse noise removal’, Signal Image Video Process., 2012, 6, (4), pp. 579–595 (doi: 10.1007/s11760-010-0186-4).
26. 26)
  - 19. Zhou, Z.: ‘Cognition and removal of impulse noise with uncertainty’, IEEE Trans. Image Process., 2012, 21, (7), pp. 3157–3161 (doi: 10.1109/TIP.2012.2189577).
27. 27)
  - 21. Syamala Jayasree, P., Raj, P., Kumar, P., Siddavatam, R., Ghrera, S.P.: ‘A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines’, Signal Image Video Process., 2012, 5, (2), pp. 1–8.
28. 28)
  - H. Hwang , R.A. Haddad . Adaptive median filters: new algorithms and results. IEEE Trans. Image Process. , 4 , 499 - 502
29. 29)
  - 25. Ground truth databases. Available from http://www.cs.washington.edu/research/imagedatabase/.
30. 30)
  - 19. Dong, Y., Chan, R.H., Xu, S.: ‘A detection statistic for random-valued impulse noise’, IEEE Trans. Image Process., 2007, 16, (4), pp. 1112–1120 (doi: 10.1109/TIP.2006.891348).

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Automatic detection and removal of high-density impulse noises

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