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PCA based image denoising using LPG

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2nd National Conference on Computing, Communication and Sensor Network
© 2011 by IJCA Journal
Number 3 - Article 5
Year of Publication: 2011
Authors:
Sabita Pal
Rina Mahakud
Madhusmita Sahoo

Sabita Pal, Rina Mahakud and Madhusmita Sahoo. PCA based Image Denoising using LPG. IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network (CCSN) (3):20-25, 2011. Full text available. BibTeX

@article{key:article,
	author = {Sabita Pal and Rina Mahakud and Madhusmita Sahoo},
	title = {PCA based Image Denoising using LPG},
	journal = {IJCA Special Issue on 2nd National Conference- Computing, Communication and Sensor Network (CCSN)},
	year = {2011},
	number = {3},
	pages = {20-25},
	note = {Full text available}
}

Abstract

This paper describes an approach of image noising and denoising by the Principal Component Analysis (PCA) method with Local Pixel Grouping (LPG). PCA fully de-correlates the original data set so that the energy of the signal will concentrate on the small subset of PCA transformed dataset. As we know energy of noise evenly spreads over the whole data set, we can easily distinguish signal from noise over PCA domain. It consists of two stages: image estimation by removing the noise and further refinement of the first stage. The noise is significantly reduced in the first stage; the LPG accuracy will be much improved in the second stage so that the final denoising result is visually much better. It also describes an algorithm capable of locating training samples selected from the local window by using block matching based LPG. Experimental results demonstrates that using LPG-PCA method the denoising performance is improved from first stage to second stage with edge preservation.

Reference

  • Lei Zhang , Weisheng Dong, David Zhang, Guangming Shib, Two-stage image denoising by principal component analysis with local pixel grouping ,Science Direct, Pattern Recognition 43 (2010) pp. 1531–1549.
  • D.L. Donoho, De-Noising by Soft Thresholding, IEEE Trans. Info. Theory 43, pp. 933-936, 1993.
  • S. Grace Chang, Bin Yu and M. Vattereli, Adaptive Wavelet Thresholding for Image Denoising and Compression, IEEE Trans. Image Processing, vol. 9, pp. 1532-1546, Sept. 2000.
  • M. Lang, H. Guo and J.E. Odegard, Noise reduction Using Undecimated Discrete wavelet transform, IEEE Signal Processing Letters, 1995.
  • R.C. Gonzalez, R.E. Woods, Digital Image Processing, second ed., Prentice- Hall, Englewood Cliffs, NJ, 2002.
  • G.Y.Chen, B.Ke´gl, Image denoising with complex ridgelets, Pattern Recognition40(2)(2007)578–585.
  • M. Elad, M. Aharon, Image denoising via sparse and redundant representa- tions over learned dictionaries, IEEE Transaction on Image Processing 15 (12) (2006) 3736–3745.
  • M. Aharon, M. Elad, A.M. Bruckstein, The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation, IEEE Transaction on Signal Processing 54 (11) (2006) 4311–4322.
  • A. Foi, V. Katkovnik, K. Egiazarian, Pointwise shape-adaptive DCT for high- quality denoising and deblocking of grayscale and color images, IEEE Transaction on Image Processing 16 (5) (2007).
  • C. Tomasi, R. Manduchi, Bilateral filtering for gray and colour images, in: Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India, 1998, pp. 839–846.
  • D. Barash, A fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation, IEEE Transaction on Pattern Analysis and Machine Intelligence 24 (6) (2002) 844–847.
  • A. Buades, B. Coll, J.M. Morel, A review of image denoising algorithms, with a new one, Multiscale Modeling Simulation 4 (2) (2005) 490–530.
  • C. Kervrann, J. Boulanger, Optimal spatial adaptation for patch based image denoising, IEEE Transaction on Image Processing 15 (10) (2006) 2866–2878.
  • K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3D transform-domain collaborative filtering, IEEE Transaction on Image Proces- sing 16 (8) (2007) 2080–2095
  • D.L.Donoho, De-noising by softthresholding, IEEE Transaction son Information Theory41(1995)613–627.
  • R.R.Coifman, D.L.Donoho, Translation –invariant de-noising, in: A.Antoniadis, G. Oppenheim (Eds.),Wavelet and Statistics, Springer, Berlin, Germany,1995.
  • M.K.Mıhc-ak, I.Kozintsev, K.Ramchandran, P.Moulin, Low-complexity image denoising based on statistical modeling of wavelet coefficients, IEEE Signal Processing Letters6(12)(1999)300–303.
  • S.G.Chang, B.Yu,M.Vetterli, Spatially adaptive wavelet thresholding with context modeling for image denoising, IEEE Transaction on Image Processing 9 (9)(2000)1522–1531.
  • 5 A.Pizurica, W.Philips, I.Lamachieu, M.Acheroy, Ajointinter-and intra scale statistical model for Bayesian wavelet based image denoising, IEEE Transaction on Image Processing 11(5)(2002)545–557.
  • L.Zhang, B.Paul, X.Wu, Hybrid inter-and intra wavelet scale image restoration,PatternRecognition36(8)(2003)1737–1746.
  • Z.Hou, Adaptive singular value decomposition in wavelet domain for image denoising, Pattern Recognition 36 (8) (2003) 1747–1763.
  • J.Portilla, V.Strela, M.J.Wainwright, E.P.Simoncelli, Image denoising using scale mixtures of Gaussian sin the wavelet domain, IEEE Transaction on Image Processing 12(11)(2003)1338–1351.
  • 9 L.Zhang, P.Bao, X.Wu, Multiscale LMMSE- based image denoising with optimal wavelet selection, IEEE Transaction on Circuits and Systems for Video Technology15(4)(2005)469–481.
  • A.Pizurica, W.Philips, Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising, IEEE Transaction on Image Processing 15 (3)(2006)654–665.
  • K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed, Academic Press, New York, 1991.
  • D.D. Muresan, T.W. Parks, Adaptive principal components and image denoising, in: Proceedings of the 2003 International Conference on Image Processing, 14–17 September, vol. 1, 2003, pp. I101–I104.