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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 – House

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Experiment 4 – Barbara

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Experiment 5 – Peppers

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Experiment 6 – Paint

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Experiment 7 – Monarch

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Experiment 8 – Bloodcell

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Experiment 9 – Tower (color)

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Experiment 10 – 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.

 

 

Reference

 

[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 – 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.