¡°Two-stage Image Denoising by Principal
Component Analysis with Local Pixel Grouping¡±
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
Experiment
1 -- Lena |
|
Experiment
2-- Cameraman |
|
Experiment
3 ¨C House |
|
Experiment
4 ¨C Barbara |
|
Experiment
5 ¨C Peppers |
|
Experiment
6 ¨C Paint |
|
Experiment
7 ¨C Monarch |
|
Experiment
8 ¨C Bloodcell |
|
Experiment
9 ¨C Tower (color) |
|
Experiment
10 ¨C Parrot (color) |
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 ¨C 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.