access icon free Image filtering method using trimmed statistics and edge preserving

© The Institution of Engineering and Technology
Received 14/09/2017, Accepted 28/01/2018, Revised 06/01/2018, Published 02/02/2018

Image filtering is to retain the details of the image as much as possible and meanwhile suppress the noise pollution to great extent. This study presents an image filtering using the truncated statistics and edge preserving. In the first step of our method, the alpha-trimmed filter is utilized to remove a variety of types of noises; in the second step, taking the image after alpha-trimmed filtering as a guide image, the local linear model between the guide image and the target image is established; in the third step, the obtained local linear model is further simplified to reduce the time complexity; and finally, using the relationship between image local variance and the global variance, the local linear model is modified to enhance the details of the image and meanwhile remove halo phenomenon. This method has three advantages: (i) it is flexible to deal with the images stained by various types of high-intensity noise; (ii) it is effective to keep the image details and profile information, and remove the halo phenomenon; and (iii) it runs in time linear in the image size, thus its computation complexity is low. Experimental results show that the proposed filter is robust and efficient.

Inspec keywords: image filtering; image denoising; computational complexity

Other keywords: noise pollution suppression; local linear model; noise removal; image filtering method; truncated statistics; time complexity reduction; alpha-trimmed filter; image local variance; global variance; high-intensity noise; target image; halo phenomenon removal; guide image; trimmed statistics; edge preserving; subsequent image processing reliability; computation complexity

Subjects: Computer vision and image processing techniques; Filtering methods in signal processing; Optical, image and video signal processing

References

    1. 1)
      • 2. Gonzalez, R.C., Woods, R.E., Stevenl, E.D.: ‘Digital image processing using MATLAB’ (Prentice-Hall, Upper Saddle River, NJ, 2013).
    2. 2)
      • 20. He, K., Sun, J., Tang, X.: ‘Guided image filtering’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (6), pp. 1397–1409.
    3. 3)
      • 32. Hanhart, P., Bernardo, M.V., Korshunov, P., et al: ‘HDR image compression: a new challenge for objective quality metrics’. Int. Workshop on Quality of Multimedia Experience, 2014, pp. 159–164.
    4. 4)
      • 13. Liu, H., Yang, C., Pan, N., et al: ‘Denoising 3D MR images by the enhanced non-local means filter for Rician noise’, Magn. Reson. Imaging, 2010, 28, (10), pp. 1485–1496.
    5. 5)
      • 16. Esakkirajan, S., Veerakumar, T., Subramanyam, A.N., et al: ‘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.
    6. 6)
      • 5. Hua, X., Guo, C., Wu, H., et al: ‘Schedulability analysis for real-time task set on resource with performance degradation and periodic rejuvenation’. IEEE Int. Conf. Embedded and Real-Time Computing Systems and Applications, 2017, pp. 197–206.
    7. 7)
      • 9. Alwassai, F, Kalyankar, N.V., Alzaky, A.A.: ‘The statistical methods of pixel-based image fusion techniques’, Int. J. Artif. Intell. Knowl. Discov., 2011, 1, (3), pp. 5–15.
    8. 8)
      • 6. Awate, S.P, Whitaker, R.T.: ‘Unsupervised, information-theoretic, adaptive image filtering for image restoration’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (3), pp. 364–376.
    9. 9)
      • 7. Randen, T., Husoy, J.H.: ‘Multichannel filtering for image texture segmentation’, Opt. Eng., 2015, 33, (8), pp. 2617–2625.
    10. 10)
      • 29. Ke, L., Yuan, W., Xiao, Y.: ‘An improved Wiener filtering method in wavelet domain’. 2008 Int. Conf. Audio, Language and Image Processing, 2008, pp. 1527–1531.
    11. 11)
      • 3. Zhang, Y., Peng, B., Wang, S., et al: ‘Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection’, Sci. Rep., 2016, 6, p. 21816.
    12. 12)
      • 10. Vermeir, T., Slowack, J., Van Leuven, S., et al: ‘Adaptive guided image filtering for screen content coding’. IEEE Int. Conf. Image Processing, 2015, pp. 5501–5505.
    13. 13)
      • 14. Sugimoto, K., Kamata, S.I.: ‘Compressive bilateral filtering’, IEEE Trans. Image Process., 2015, 24, (11), pp. 3357–3369.
    14. 14)
      • 12. Song, H., Huang, B., Zhang, K.: ‘Shadow detection and reconstruction in high-resolution satellite images via morphological filtering and example-based learning’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (52), pp. 2545–2554.
    15. 15)
      • 28. Chen, B., Xing, L., Liang, J., et al: ‘Steady-state mean-square error analysis for adaptive filtering under the maximum correntropy criterion’, IEEE Signal Process. Lett., 2014, 21, (7), pp. 880–884.
    16. 16)
      • 38. Porikli, F.: ‘Constant time O(1) bilateral filtering’. IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 1–8.
    17. 17)
      • 22. 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.
    18. 18)
      • 17. Bednar, J.B., Watt, T.L.: ‘Alpha-trimmed means and their relationship to median filters’, IEEE Trans. Acoust. Speech Signal Process., 1984, 32, (1), pp. 145–153.
    19. 19)
      • 21. Li, Z., Zheng, J., Zhu, Z., et al: ‘Weighted guided image filtering’, IEEE Trans. Image Process., 2015, 24, (1), pp. 120–129.
    20. 20)
      • 35. Cai, W., Chen, S., Zhang, D.: ‘Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation’, Pattern Recognit., 2007, 40, (3), pp. 825–838.
    21. 21)
      • 8. Haque, S.M., Pai, G.P., Govindu, V.M.: ‘Symmetric smoothing filters from global consistency constraints’, IEEE Trans. Image Process., 2015, 24, (5), pp. 1536–1548.
    22. 22)
      • 34. Kannan, S., Nalini, G., Gurusamy, V.: ‘Review on image segmentation techniques’, Pattern Recognit., 2015, 26, (9), pp. 1277–1294.
    23. 23)
      • 27. Alejo, R., García, V., Pacheco-Sánchez, J.H.: ‘An efficient over-sampling approach based on mean square error back-propagation for dealing with the multi-class imbalance problem’, Neural Process. Lett., 2015, 42, (3), pp. 603–617.
    24. 24)
      • 39. Yang, Q., Tan, K.H., Ahuja, N.: ‘Real-time O(1) bilateral filtering’. IEEE Conf. Computer Vision and Pattern Recognition, 2009, pp. 557–564.
    25. 25)
      • 24. Zhang, X., Xiong, Y.: ‘Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter’, IEEE Signal Process. Lett., 2009, 16, (4), pp. 295–298.
    26. 26)
      • 23. Chen, P.Y., Lien, C.Y.: ‘An efficient edge-preserving algorithm for removal of salt-and-pepper noise’, IEEE Signal Process. Lett., 2008, 15, pp. 833–836.
    27. 27)
      • 4. Li, Z., Cheng, Y., Tang, K., et al: ‘A salt & pepper noise filter based on local and global image information’, Neurocomputing, 2015, 159, pp. 172–185.
    28. 28)
      • 26. Wang, Z.Z., Yong, J.H.: ‘Texture analysis and classification with linear regression model based on wavelet transform’, IEEE Trans. Image Process., 2008, 17, (8), pp. 1421–1430.
    29. 29)
      • 1. Fujita, K., Suzuki, M.: ‘Image processing device, image processing method, and storage medium’, Med. J. Aust., 2017, 156, (2), pp. 356–363.
    30. 30)
      • 19. Durand, F., Dorsey, J.: ‘Fast bilateral filtering for the display of high-dynamic-range images’, ACM Trans. Graph., 2002, 21, (3), pp. 257–266.
    31. 31)
      • 33. Durand, F., Dorsey, J.: ‘Fast bilateral filtering for the display of high-dynamic-range images’. ACM Conf. Computer Graphics and Interactive Techniques, 2002, pp. 257–266.
    32. 32)
      • 30. Farbman, Z., Fattal, R., Lischinski, D., et al: ‘Edge-preserving decompositions for multi-scale tone and detail manipulation’, ACM Trans. Graph., 2008, 27, (3), pp. 1–10.
    33. 33)
      • 18. Zhang, M., Gunturk, B.K.: ‘Multiresolution bilateral filtering for image denoising’, IEEE Trans. Image Process., 2009, 17, (12), pp. 2324–2333.
    34. 34)
      • 11. Ellmauthaler, A., Pagliari, C.L., Da, S.E.: ‘Multiscale image fusion using the undecimated wavelet transform with spectral factorization and nonorthogonal filter banks’, IEEE Trans. Image Process., 2013, 22, (3), pp. 1005–1017.
    35. 35)
      • 36. Ganesan, P., Rajini, V.: ‘YIQ color space based satellite image segmentation using modified FCM clustering and histogram equalization’. IEEE Int. Conf. Advances in Electrical Engineering, 2014, pp. 1–5.
    36. 36)
      • 25. Tanaka, H., Watada, J.: ‘Possibilistic linear systems and their application to the linear regression model’, Fuzzy Sets Syst.., 2013, 27, (3), pp. 275–289.
    37. 37)
      • 31. Tomasi, C., Manduchi, R.: ‘Bilateral filtering for gray and color images’. IEEE Int. Conf. Computer Vision, 1998, pp. 839–846.
    38. 38)
      • 15. Liu, G., Zhong, H.: ‘Nonlocal means filter for polarimetric SAR data despeckling based on discriminative similarity measure’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (2), pp. 514–518.
    39. 39)
      • 37. Yeo, B.T., Sabuncu, M.R., Desikan, R., et al: ‘Effects of registration regularization and atlas sharpness on segmentation accuracy’, Med. Image Anal., 2008, 12, (5), pp. 603–615.
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