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A practical parallel implementation for TDLMS image filter on multi-core processor

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Abstract

In this study, parallel implementation of adaptive image filtering algorithm based on two-dimensional least mean square method (TDLMS) where the weights are continuously adjusted during filtering was realized by proposed design considerations. Despite its strictly sequential structure, the effect of a pixel on weights vanishes as the filter mask progresses. Based on this property, the load of filtering algorithm is allocated to threads by splitting the input image into sub-blocks. Due to the discontinuities, the crossing distortions between sub-blocks were eliminated using weight synchronization with the neighbor sub-block. Performance evaluations for various sizes of images were realized on a computer with multi-core processor using open multiprocessing library. In spite of the sequential nature of the algorithm, results show that the parallel implementation provides significant improvements in terms of both speedup and parallel efficiency.

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References

  1. Kutila, M., Viitanen, J.: Parallel image compression and analysis with wavelets. Int. J. Signal Process. 1(1–4), 65–68 (2004)

    Google Scholar 

  2. Bräunl, T.: Tutorial in data parallel image processing. AJIIPS 6(3), 164–174 (2001)

    Google Scholar 

  3. Kepner, J.: A multi-threaded fast convolver for dynamically parallel image filtering. J. Parallel Distrib. Comput. 63, 360–372 (2003)

    Article  MATH  Google Scholar 

  4. Gorder, P.F.: Multicore processors for science and engineering. IEEE Comput. Sci. Eng. 9(2), 3–7 (2007)

    Article  Google Scholar 

  5. Andrews, G.: Foundations of multithreaded, parallel, and distributed programming. Addison-Wesley, Harlow (2000)

    Google Scholar 

  6. Warg, F., Stenstrom, P.: Dual-thread speculation: a simple approach to uncover thread-level parallelism on a simultaneous multithreaded processor. Int. J. Parallel Prog. 36, 166–183 (2008)

    Article  MATH  Google Scholar 

  7. Yu, S., Clement, M., Snell, Q., Morse, B.: Parallel algorithms for image convolution. In: International Conference on Parallel and Distributed Techniques and Applications, Las Vegas (1998)

  8. Grimshaw, A.S., Strayer, W.T., Narayan, P.: Dynamic, object-oriented parallel processing. IEEE Parallel Distrib. Technol. Syst. Appl. (1993)

  9. Karpovich, J.F., Judd, M., Strayer, W.T., Grimshaw, A.S.: A parallel object-oriented framework for stencil algorithms. IEEE Int. Symp. High Perform. Distrib. Comput. pp. 34–41 (1993)

  10. Nakariyakul, S.: Fast spatial averaging: an efficient algorithm for 2D mean filtering. J. Supercomput. 65(1), 262–273 (2013)

    Article  Google Scholar 

  11. Hadhoud, M.M., Thomas, D.W.: The two-dimensional adaptive LMS (TDLMS) algorithm. IEEE Trans. Circuits Syst. 35(5), 485–494 (1988)

    Article  Google Scholar 

  12. Mikhael, W.B., Ghosh, S.M.: Two dimensional block adaptive filtering algorithms. In: Proceedings of ISCAS’92, pp. 1219–1222 (1992)

  13. Kinjo, S., Oshiro, M., Ochi, H.: A new two-dimensional parallel block adaptive filter with reduced computational complexity, icassp, vol. 3, p. 2305. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’97)-Vol 3 (1997)

  14. Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, Upper Saddle River (2008)

    Google Scholar 

  15. OpenMP architecture review board: OpenMP C and C++ application program interface. Version 3.1 (July 2011)

  16. Chapman, B., Jost, G., van der Pas, R.: Using OpenMP. MIT Press, Cambridge (2007)

    Google Scholar 

  17. Widrow, B., Glover, Jr. J.R., McCool, J.M., Kaunitz, J., Williams, C.S., Hearn, R.H., Zeidler, J.R., Dong, Jr. E., Goodlin, R.C.: Adaptive noise cancelling: principles and applications. In: Proceedings of the IEEE, 63(12):1692–1716 (1975)

  18. Ohki, M., Hashiguchi, S.: Two-dimensional LMS adaptive filters. IEEE Trans. Consum. Electron. 37(1), 66–73 (1991)

    Article  Google Scholar 

  19. Abadi, M.S.E., Far, A.M., Ebrahimpour, R., Kabir, E.: Image restoration using two dimensional fast euclidean direction search based adaptive algorithm. Adv. Soft Comput. 29, 182–191 (2005)

    Article  Google Scholar 

  20. Widrow, B., Stearns, S.D.: Adaptive signal processing. Prentice Hall, Englewood Cliffs (1985)

    MATH  Google Scholar 

  21. Widrow, B., McCool, J.M., Larimore, M.G., Johnson, Jr. C.R.: Stationary and nonstationary learning characteristics of the LMS adaptive filters. In: Proceedings of IEEE, 64:1151–1162 (1976)

  22. Huynh-Thu, Q., Ghanbari, M.: Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44(13), 800–801 (2008)

    Article  Google Scholar 

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  1. Department of Computer Engineering of Technology Faculty, Sakarya University, 54187, Sakarya, Turkey

    Devrim Akgün

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  1. Devrim Akgün
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Akgün, D. A practical parallel implementation for TDLMS image filter on multi-core processor. J Real-Time Image Proc 13, 249–260 (2017). https://doi.org/10.1007/s11554-014-0397-y

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  • DOI: https://doi.org/10.1007/s11554-014-0397-y

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