PCA Based Image Enhancement in Wavelet Domain
International Journal of Engineering Trends and Technology (IJETT) | |
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© 2012 by IJETT Journal | ||
Volume-3 Issue-1 |
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Year of Publication : 2012 | ||
Authors : Vikas D Patil , Sachin D. Ruikar |
Citation
Vikas D Patil , Sachin D. Ruikar. "PCA Based Image Enhancement in Wavelet Domain". International Journal of Engineering Trends and Technology (IJETT). V3(1):59-63 Jan-Feb 2012. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
This paper demonstrates a methodology of image enhancement that uses principle component analysis (PCA) in wavelet domain . 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. The energy of random noise evenly spreads over the whole data set, we can easily distinguish signal from random noise over PCA domain. It consists of two stages: image enhancement by removing the random noise and further refinement of the first stage. The random noise is significantly reduced in the first stage; the Local Pixel Grouping (LPG) accuracy will be much improved in the second stage so that the final enhancement result is visually much better. The LPG - PCA e nhance procedure is used to improve the image quality from first stage to second stage with edge preservation. The wavelet thresholding methods used for removing random noise has been researched extensively due to its effectiveness and simplicity. However, not much has been done to make the threshold values adaptive to the spatially changing statistics of images. Such adaptivity can improve the wavelet th resholding performance because it allows additional local information of the image (such as the identification of smooth or edge regions) to be incorporated into the algorithm. We compare this two - stage process with traditional principal component analysi s and find that the results of the new structure are closer to the structure of traditional quality of image, its purity and descriptors than traditional principal component analysis.
References
[1] R.C.Gonzalez, R.E. Woods, Digital Image Processing,second ed ition ., Prentice - Hall, EnglewoodCliffs,NJ,2002.
[2] D.L.Donoho, De - noising by soft thresholding, IEEE Transactions on Information Theory 41(1995)613 – 627.
[3] R.R.Coifman,D.L.Donoho,Translation - invariant denoising, in: A.Antoniadis,G. O ppenheim (Eds.), Wavelet and Statistics, Springer, Berlin, Germany,1995.
[4] M.K.M?hc - 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.
[5] 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.
[6] A.Pizurica,W. Philips, I. Lamachieu, M. Acheroy, A jointinte and intra scale statistical model for Bayesian wavelet based image denoising, IEEE Transaction on Image Processing11(5)(2002)545 – 557.
[7] L.Zhang, B. Paul, X. Wu, Hybridinter and intra wavelet scale image restoration, Pattern Recognition 36(8)(2003)1737 – 1746.
[8] Z.Hou,Adaptive singular value decomposition in wavelet domain for image denoising, Pattern Recognition36(8)(2003)1747 – 1763.
[9] J.Portilla,V. Strela, M.J.Wainwright,E. P.Simoncelli, Image denoising using scale mixtures of Gaussians in the wavelet domain, IEEE Transaction on Image Processing 12(11)(2003)1338 – 1351
[10] L.Zhang,P.Bao,X.Wu,MultiscaleLMMSE - based image denoising with optimal wavelet selection, IEEE Transaction on Circuits and Systems for Vide o Technology15(4)(2005)469 – 481.
[11] A.Pizurica,W.Philips, Estimating the probability of the presence a signal of interest in multire solution single and multi image denoising, IEEE Transaction on Image Processing15(3)(2006)654 – 665.
[12] J.L.Starck,E.J.Candes,D.L .Donoho,The curvelet transform for image denoising, IEEE Transaction on Image Processing11(6)(2002)670 – 684.
[13] G.Y.Chen,B.Ke ?gl, Image denoising with complex ridgelets, Pattern Recognition 40(2)(2007)578 – 585.
[14] M.Elad,M.Aharon, Image denoising viasparse and re dundant representations over learn eddiction aries, IEEE Transaction on Image Processing15(12) (2006)3736 – 3745.
[15] S . Mallat, A Wavelet Tour of Signal Processing, Academic Press, New York, 1998.
[16] D.D.Muresan,T.W.Parks, Adaptive principal components and ima ge denoising, in: Proceedings of the 2003 International Conference on Image Processing,14 – 17September,vol.1,2003,pp.I101 – I104.
[17] M .Aharon,M.Elad,A.M.Bruckstein,The K - SVD: an algorithm for designing of over complete diction arise for sparse representation, I EEE Transaction on Signal Processing54(11)(2006)4311 – 4322.
[18] A.Foi, V.Katkovnik, K.Egiazarian, Pointwise shape adaptive DCT for high quality denoising and de blocking of grayscale and color images, IEEE Transaction on Image Processing 16(5)(2007).
[19] S. Hayki n , Neural Networks: A Comprehensive Foundation , 2nd ed. Englewood Cliffs, NJ: Prentice - Hall, 1999.
[20] K. Fukunaga , Introduction to Statistical Pattern Recognition , 2nd ed. New York: Academic, 1991
Keywords
Wavelet, Wavelet Transform (WT), Local Pixel Grouping (LPG), Principal Components Analysis