http://iet.metastore.ingenta.com
1887

Image filtering method using trimmed statistics and edge preserving

Image filtering method using trimmed statistics and edge preserving

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

References

    1. 1)
      • K. Fujita , M. Suzuki .
        1. Fujita, K., Suzuki, M.: ‘Image processing device, image processing method, and storage medium’, Med. J. Aust., 2017, 156, (2), pp. 356363.
        . Med. J. Aust. , 2 , 356 - 363
    2. 2)
      • R.C. Gonzalez , R.E. Woods , E.D. Stevenl . (2013)
        2. Gonzalez, R.C., Woods, R.E., Stevenl, E.D.: ‘Digital image processing using MATLAB’ (Prentice-Hall, Upper Saddle River, NJ, 2013).
        .
    3. 3)
      • Y. Zhang , B. Peng , S. Wang .
        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.
        . Sci. Rep. , 21816
    4. 4)
      • Z. Li , Y. Cheng , K. Tang .
        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. 172185.
        . Neurocomputing , 172 - 185
    5. 5)
      • X. Hua , C. Guo , H. Wu .
        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. 197206.
        . IEEE Int. Conf. Embedded and Real-Time Computing Systems and Applications , 197 - 206
    6. 6)
      • S.P Awate , R.T. Whitaker .
        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. 364376.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 3 , 364 - 376
    7. 7)
      • T. Randen , J.H. Husoy .
        7. Randen, T., Husoy, J.H.: ‘Multichannel filtering for image texture segmentation’, Opt. Eng., 2015, 33, (8), pp. 26172625.
        . Opt. Eng. , 8 , 2617 - 2625
    8. 8)
      • S.M. Haque , G.P. Pai , V.M. Govindu .
        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. 15361548.
        . IEEE Trans. Image Process. , 5 , 1536 - 1548
    9. 9)
      • F Alwassai , N.V. Kalyankar , A.A. Alzaky .
        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. 515.
        . Int. J. Artif. Intell. Knowl. Discov. , 3 , 5 - 15
    10. 10)
      • T. Vermeir , J. Slowack , S. Van Leuven .
        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. 55015505.
        . IEEE Int. Conf. Image Processing , 5501 - 5505
    11. 11)
      • A. Ellmauthaler , C.L. Pagliari , S.E. Da .
        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. 10051017.
        . IEEE Trans. Image Process. , 3 , 1005 - 1017
    12. 12)
      • H. Song , B. Huang , K. Zhang .
        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. 25452554.
        . IEEE Trans. Geosci. Remote Sens. , 52 , 2545 - 2554
    13. 13)
      • H. Liu , C. Yang , N. Pan .
        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. 14851496.
        . Magn. Reson. Imaging , 10 , 1485 - 1496
    14. 14)
      • K. Sugimoto , S.I. Kamata .
        14. Sugimoto, K., Kamata, S.I.: ‘Compressive bilateral filtering’, IEEE Trans. Image Process., 2015, 24, (11), pp. 33573369.
        . IEEE Trans. Image Process. , 11 , 3357 - 3369
    15. 15)
      • G. Liu , H. Zhong .
        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. 514518.
        . IEEE Geosci. Remote Sens. Lett. , 2 , 514 - 518
    16. 16)
      • S. Esakkirajan , T. Veerakumar , A.N. Subramanyam .
        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. 287290.
        . IEEE Signal Process. Lett. , 5 , 287 - 290
    17. 17)
      • J.B. Bednar , T.L. Watt .
        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. 145153.
        . IEEE Trans. Acoust. Speech Signal Process. , 1 , 145 - 153
    18. 18)
      • M. Zhang , B.K. Gunturk .
        18. Zhang, M., Gunturk, B.K.: ‘Multiresolution bilateral filtering for image denoising’, IEEE Trans. Image Process., 2009, 17, (12), pp. 23242333.
        . IEEE Trans. Image Process. , 12 , 2324 - 2333
    19. 19)
      • F. Durand , J. Dorsey .
        19. Durand, F., Dorsey, J.: ‘Fast bilateral filtering for the display of high-dynamic-range images’, ACM Trans. Graph., 2002, 21, (3), pp. 257266.
        . ACM Trans. Graph. , 3 , 257 - 266
    20. 20)
      • K. He , J. Sun , X. Tang .
        20. He, K., Sun, J., Tang, X.: ‘Guided image filtering’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (6), pp. 13971409.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 1397 - 1409
    21. 21)
      • Z. Li , J. Zheng , Z. Zhu .
        21. Li, Z., Zheng, J., Zhu, Z., et al: ‘Weighted guided image filtering’, IEEE Trans. Image Process., 2015, 24, (1), pp. 120129.
        . IEEE Trans. Image Process. , 1 , 120 - 129
    22. 22)
      • K.K.V. Toh , N.A.M. Isa .
        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. 281284.
        . IEEE Signal Process. Lett. , 3 , 281 - 284
    23. 23)
      • P.Y. Chen , C.Y. Lien .
        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. 833836.
        . IEEE Signal Process. Lett. , 833 - 836
    24. 24)
      • X. Zhang , Y. Xiong .
        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. 295298.
        . IEEE Signal Process. Lett. , 4 , 295 - 298
    25. 25)
      • H. Tanaka , J. Watada .
        25. Tanaka, H., Watada, J.: ‘Possibilistic linear systems and their application to the linear regression model’, Fuzzy Sets Syst.., 2013, 27, (3), pp. 275289.
        . Fuzzy Sets Syst.. , 3 , 275 - 289
    26. 26)
      • Z.Z. Wang , J.H. Yong .
        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. 14211430.
        . IEEE Trans. Image Process. , 8 , 1421 - 1430
    27. 27)
      • R. Alejo , V. García , J.H. Pacheco-Sánchez .
        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. 603617.
        . Neural Process. Lett. , 3 , 603 - 617
    28. 28)
      • B. Chen , L. Xing , J. Liang .
        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. 880884.
        . IEEE Signal Process. Lett. , 7 , 880 - 884
    29. 29)
      • L. Ke , W. Yuan , Y. Xiao .
        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. 15271531.
        . 2008 Int. Conf. Audio, Language and Image Processing , 1527 - 1531
    30. 30)
      • Z. Farbman , R. Fattal , D. Lischinski .
        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. 110.
        . ACM Trans. Graph. , 3 , 1 - 10
    31. 31)
      • C. Tomasi , R. Manduchi .
        31. Tomasi, C., Manduchi, R.: ‘Bilateral filtering for gray and color images’. IEEE Int. Conf. Computer Vision, 1998, pp. 839846.
        . IEEE Int. Conf. Computer Vision , 839 - 846
    32. 32)
      • P. Hanhart , M.V. Bernardo , P. Korshunov .
        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. 159164.
        . Int. Workshop on Quality of Multimedia Experience , 159 - 164
    33. 33)
      • F. Durand , J. Dorsey .
        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. 257266.
        . ACM Conf. Computer Graphics and Interactive Techniques , 257 - 266
    34. 34)
      • S. Kannan , G. Nalini , V. Gurusamy .
        34. Kannan, S., Nalini, G., Gurusamy, V.: ‘Review on image segmentation techniques’, Pattern Recognit., 2015, 26, (9), pp. 12771294.
        . Pattern Recognit. , 9 , 1277 - 1294
    35. 35)
      • W. Cai , S. Chen , D. Zhang .
        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. 825838.
        . Pattern Recognit. , 3 , 825 - 838
    36. 36)
      • P. Ganesan , V. Rajini .
        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. 15.
        . IEEE Int. Conf. Advances in Electrical Engineering , 1 - 5
    37. 37)
      • B.T. Yeo , M.R. Sabuncu , R. Desikan .
        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. 603615.
        . Med. Image Anal. , 5 , 603 - 615
    38. 38)
      • F. Porikli .
        38. Porikli, F.: ‘Constant time O(1) bilateral filtering’. IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 18.
        . IEEE Conf. Computer Vision and Pattern Recognition , 1 - 8
    39. 39)
      • Q. Yang , K.H. Tan , N. Ahuja .
        39. Yang, Q., Tan, K.H., Ahuja, N.: ‘Real-time O(1) bilateral filtering’. IEEE Conf. Computer Vision and Pattern Recognition, 2009, pp. 557564.
        . IEEE Conf. Computer Vision and Pattern Recognition , 557 - 564
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0470
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0470
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address