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Experimental results of paper


※Two-stage Image Denoising by Principal Component Analysis with Local Pixel Grouping§

by Lei Zhang, Weisheng Dong, David Zhang and Guangming Shi


Pattern Recognition, vol. 43, issue 4, pp. 1531-1549, April 2010.


Paper:                   download here.

Matlab code:          download here.


The Matlab source code of our LPG-PCA denoising algorithm is optimized. Now it requires only 1~2 mins to denoise an image of 256 by 256!


We label


the denoising method [1] as ※pbshrink§;

the denoising method [2] as ※mixscale§;

the denoising method [3] as ※ksvd§;

the denoising method [4] as ※bm3d§;

and the proposed LPG-PCA-based denoising method as ※lpgpca§.


With these labels, the denoising result by using the method ※lpgpca§ of noisy image Lena with =20 is saved as ※lena20_lpgpca.tif§, and similarly for other methods.


Experiment 1 -- Lena

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Experiment 2-- Cameraman

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Experiment 3 每 House

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Experiment 4 每 Barbara

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Experiment 5 每 Peppers

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Experiment 6 每 Paint

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Experiment 7 每 Monarch

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Experiment 8 每 Bloodcell

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Experiment 9 每 Tower (color)

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Experiment 10 每 Parrot (color)

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The PSNR (dB) and SSIM results of the denoised images at different noise levels and by different schemes. The value in the parenthesis is the SSIM [5] measurement.





[1]      A. Pizurica and W. Philips, ※Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising,§ IEEE Trans. on Image Processing, vol. 15, no. 3, pp. 654-665, March 2006.

[2]      J. Portilla, V. Strela, M. J.  Wainwright and E. P. Simoncelli, ※Image denoising using scale mixtures of Gaussians in the wavelet domain,§ IEEE Trans. on Image Processing, vol. 12, no. 11, pp. 1338 每 1351, Nov. 2003.

[3]      M. Aharon, M. Elad, and A.M. Bruckstein, The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation IEEE Trans. On Signal Processing, vol. 54, no. 11, pp. 4311-4322, November 2006.

[4]      K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, ※Image denoising by sparse 3D transform-domain collaborative filtering,§ IEEE Trans. Image Processing, vol. 16, no. 8, pp. 2080-2095, August 2007.

[5]      Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, ※Image quality assessment: From error visibility to structural similarity,§ IEEE Transactions on Image Processing, vol. 13, no. 4, Apr. 2004.