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2 boyutlu sıfıra çeken LMS algoritmasıyla görüntü iyileştirme

Year 2019, Volume: 25 Issue: 5, 539 - 545, 21.10.2019

Gülden Eleyan , Muhammed Salman

Abstract

Bu yazıda, iki boyutlu
en küçük kare algoritmasının (2D-LMS) maliyet fonksiyonuna seyrekliği farkeden
l1-norm ceza terimi yükleyen yeni bir 2D sıfıra çeken en küçük
ortalama kare (ZALMS) uyarlamalı filtreyi önermekteyiz. 2D-LMS ve BM3D
algoritmaları ile karşılaştırmalar hem seyrek hem de seyrek olmayan
görüntülerde yürütülmüştür. Simülasyon sonuçları, önerilen algoritmanın hem
yatay hem de dikey doğrultuda filtre katsayılarının güncellenmesinde iyi
yeteneklere sahip olduğunu göstermiştir ve performansı düşük hesaplama zamanına
sahip 2D-LMS algoritması ile aynı/daha iyidir. Ancak 2D-ZALMS, BM3D
algoritmasından daha iyi performans göstermektedir.

Keywords

LMS Görüntü iyileştirme Seyrek sinyaller

References

  • Widrow B, Hoff M.E. “Associative storage and retrieval of digital information in networks of adaptive neurons”. Biological Prototypes and Synthetic Systems, 160-160, 1962.
  • Haykin S. Adaptive Filter Theory. 5th ed. USA, Pearson, 2014.
  • Hussain U, Kumar S. “Adaptive echo canceller using LMS algorithm”. International Journal of Advance Engineering and Research Development, 2(8), 54-59, 2015.
  • Wang X, Gu Y, Chen L. “Proof of convergence and performance analysis for sparse recovery via zero-point attracting projection”. IEEE Transactions Signal Processing, 60(8), 4081-4093, 2012.
  • Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S. “Sparse representation for computer vision and pattern recognition”. IEEE Proceedings, 98(6), 1031-1044, 2010.
  • Czink N, Yin X, Ozcelik H, Herdin M, Bonek E, Fleury B. “Cluster characteristics in a MIMO indoor propagation environment”. IEEE Transactions on Wireless Communications, 6 (4), 1465–1475, 2007.
  • Vuokko L, Kolmonen VM, Salo J, Vainikainen P. “Measurement of large-scale cluster power characteristics for geometric channel models”. IEEE Transactions on Antennas and Propagation, 55(11), 3361–3365, 2007.
  • Gui G, Peng W, Adachi F. “Improved adaptive sparse channel estimation based on the least mean square algorithm”. IEEE Wireless Communication and Networking Conference, Shanghai, China, 07-10 April 2013.
  • Chen Y, Gu Y, Hero AO. “Sparse LMS for system identification”. IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, 4 April 2009.
  • Wang X, Gu Y, Chen L. “Proof of Convergence and Performance Analysis for Sparse Recovery via Zero-Point Attracting Projection”. IEEE Transactions on Signal Processing, 60(8), 4081-4093. 2012.
  • Taheri O, Vorobyov SA. “Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean square”. IEEE Conference on Acoustics Speech and Signal Processing (ICASSP), Prague, Czech Republic, 22-27 May 2011.
  • Gu Y, Jin J, Mei S. “l0-Norm constraint LMS algorithm for sparse system identification”. IEEE Signal Processing Letters, 16(9), 774–777. 2009.
  • Su G, Jin J, Gu Y, Wang J. “Performance analysis of l0-norm constraint least mean square algorithm”. IEEE Transactions on Signal Processing, 60(5), 2223-2235, 2012.
  • Mancera L, Portilla J. “l0-Norm-Based sparse representation through alternate projections”. IEEE International Conference on Image Processing, Georgia, USA, 08-11 October 2006.
  • Hadhoud MM, Thomas DW. “The two-dimensional adaptive LMS (TDLMS) algorithm”. IEEE Transactions on Circuits and Systems , 35(5), 485-494, 1988.
  • Abadi MSE, Nikbakht S. “Image denoising with two-dimensional adaptive filter algorithms”. Iranian Journal of Electrical & Electronic Engineering, 7, 84-105. 2011.
  • Zhang X, Jiang T, Li Y. “2D sparsity-information-aided least mean square algorithm for sparse image de-noising”. Wireless Communications & Signal Processing Conference, Yangzhou, China, 13-15 October 2016.
  • Kockanat S, Karaboga N. “A novel 2D-ABC adaptive filter algorithm: A comparative study”. Journal of Digital Signal Processing, 40, 140-153, 2015.
  • Lebrun M. “An analysis and implementation of the BM3D image denoising method”. Image Processing On Line, 2, 175-213, 2012.
  • Muneyasu M, Uemoto E, Hinamoto T. “A novel 2-D adaptive filter based on the 1-D RLS algorithm”. IEEE International Symposium Circuits and Systems, Hong Kong, 12 June 1997.
  • Yang J. Adaptive Filter Design for Sparse Signal Estimation. Ph.D. Dissertation, University of Minnesota, Minnesota, USA, 2011.

Image denoising with two-dimensional zero attracting LMS algorithm

Year 2019, Volume: 25 Issue: 5, 539 - 545, 21.10.2019

Gülden Eleyan , Muhammed Salman

Abstract

In this paper, we propose a new two-dimensional
(2D) zero-attracting least-mean-square (ZALMS) adaptive filter by imposing a
sparsity aware l1-norm penalty term into the cost function of the
2D-LMS algorithm. Comparisons with 2D-LMS and BM3D algorithms were conducted
both on sparse and non-sparse images. The carried-out simulations show that the
proposed algorithm has good capabilities in updating the filter coefficients
along both horizontal and vertical directions, and its performance is similar
with the 2D-LMS algorithm with lower computation time. But 2D-ZALMS performs
better than BM3D algorithm.

Keywords

LMS Image restoration Sparse signals

References

  • Widrow B, Hoff M.E. “Associative storage and retrieval of digital information in networks of adaptive neurons”. Biological Prototypes and Synthetic Systems, 160-160, 1962.
  • Haykin S. Adaptive Filter Theory. 5th ed. USA, Pearson, 2014.
  • Hussain U, Kumar S. “Adaptive echo canceller using LMS algorithm”. International Journal of Advance Engineering and Research Development, 2(8), 54-59, 2015.
  • Wang X, Gu Y, Chen L. “Proof of convergence and performance analysis for sparse recovery via zero-point attracting projection”. IEEE Transactions Signal Processing, 60(8), 4081-4093, 2012.
  • Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S. “Sparse representation for computer vision and pattern recognition”. IEEE Proceedings, 98(6), 1031-1044, 2010.
  • Czink N, Yin X, Ozcelik H, Herdin M, Bonek E, Fleury B. “Cluster characteristics in a MIMO indoor propagation environment”. IEEE Transactions on Wireless Communications, 6 (4), 1465–1475, 2007.
  • Vuokko L, Kolmonen VM, Salo J, Vainikainen P. “Measurement of large-scale cluster power characteristics for geometric channel models”. IEEE Transactions on Antennas and Propagation, 55(11), 3361–3365, 2007.
  • Gui G, Peng W, Adachi F. “Improved adaptive sparse channel estimation based on the least mean square algorithm”. IEEE Wireless Communication and Networking Conference, Shanghai, China, 07-10 April 2013.
  • Chen Y, Gu Y, Hero AO. “Sparse LMS for system identification”. IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, 4 April 2009.
  • Wang X, Gu Y, Chen L. “Proof of Convergence and Performance Analysis for Sparse Recovery via Zero-Point Attracting Projection”. IEEE Transactions on Signal Processing, 60(8), 4081-4093. 2012.
  • Taheri O, Vorobyov SA. “Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean square”. IEEE Conference on Acoustics Speech and Signal Processing (ICASSP), Prague, Czech Republic, 22-27 May 2011.
  • Gu Y, Jin J, Mei S. “l0-Norm constraint LMS algorithm for sparse system identification”. IEEE Signal Processing Letters, 16(9), 774–777. 2009.
  • Su G, Jin J, Gu Y, Wang J. “Performance analysis of l0-norm constraint least mean square algorithm”. IEEE Transactions on Signal Processing, 60(5), 2223-2235, 2012.
  • Mancera L, Portilla J. “l0-Norm-Based sparse representation through alternate projections”. IEEE International Conference on Image Processing, Georgia, USA, 08-11 October 2006.
  • Hadhoud MM, Thomas DW. “The two-dimensional adaptive LMS (TDLMS) algorithm”. IEEE Transactions on Circuits and Systems , 35(5), 485-494, 1988.
  • Abadi MSE, Nikbakht S. “Image denoising with two-dimensional adaptive filter algorithms”. Iranian Journal of Electrical & Electronic Engineering, 7, 84-105. 2011.
  • Zhang X, Jiang T, Li Y. “2D sparsity-information-aided least mean square algorithm for sparse image de-noising”. Wireless Communications & Signal Processing Conference, Yangzhou, China, 13-15 October 2016.
  • Kockanat S, Karaboga N. “A novel 2D-ABC adaptive filter algorithm: A comparative study”. Journal of Digital Signal Processing, 40, 140-153, 2015.
  • Lebrun M. “An analysis and implementation of the BM3D image denoising method”. Image Processing On Line, 2, 175-213, 2012.
  • Muneyasu M, Uemoto E, Hinamoto T. “A novel 2-D adaptive filter based on the 1-D RLS algorithm”. IEEE International Symposium Circuits and Systems, Hong Kong, 12 June 1997.
  • Yang J. Adaptive Filter Design for Sparse Signal Estimation. Ph.D. Dissertation, University of Minnesota, Minnesota, USA, 2011.
There are 21 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Gülden Eleyan

Muhammed Salman This is me

Publication Date October 21, 2019
Published in Issue Year 2019 Volume: 25 Issue: 5

Cite

APA Eleyan, G., & Salman, M. (2019). Image denoising with two-dimensional zero attracting LMS algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(5), 539-545.
AMA Eleyan G, Salman M. Image denoising with two-dimensional zero attracting LMS algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. October 2019;25(5):539-545.
Chicago Eleyan, Gülden, and Muhammed Salman. “Image Denoising With Two-Dimensional Zero Attracting LMS Algorithm”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 25, no. 5 (October 2019): 539-45.
EndNote Eleyan G, Salman M (October 1, 2019) Image denoising with two-dimensional zero attracting LMS algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 25 5 539–545.
IEEE G. Eleyan and M. Salman, “Image denoising with two-dimensional zero attracting LMS algorithm”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 25, no. 5, pp. 539–545, 2019.
ISNAD Eleyan, Gülden - Salman, Muhammed. “Image Denoising With Two-Dimensional Zero Attracting LMS Algorithm”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 25/5 (October 2019), 539-545.
JAMA Eleyan G, Salman M. Image denoising with two-dimensional zero attracting LMS algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2019;25:539–545.
MLA Eleyan, Gülden and Muhammed Salman. “Image Denoising With Two-Dimensional Zero Attracting LMS Algorithm”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 25, no. 5, 2019, pp. 539-45.
Vancouver Eleyan G, Salman M. Image denoising with two-dimensional zero attracting LMS algorithm. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2019;25(5):539-45.

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