Image denoising using the TV-means algorithm


General description and related papers

On this page you will find examples illustrating the TV-means image denoising algorithm. It is a patch-based image denoising using Total Variation regularization according to the following theoretical scheme: This algorithm combines two famous (and very different) image denoising methods: Total Variation denoising [1] and NL-means denoising [2]. It exploits the strenghts of both methods and manages to produce better results than each of them, as illustrated below. For mode precise details about the method and the algorithm, see

  C. Louchet, L. Moisan, "Total Variation as a local filter", SIAM Journal on Imaging Sciences, vol 4:2, pp. 651-694, 2011.
 
download: published version PDF BibTeX SIAM


References:

[1] L. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D, vol. 60, n. 1-4, pp. 259-268, 1992.

[2] A. Buades, B. Coll, J.-M. Morel, A review of image denoising algorithms, with a new one, SIAM Multiscale Modeling and Simulation, vol. 4, n. 2, pp. 490-530 (electronic), 2005.


Examples

Examples below compare three methods: Total Variation, NL-means, and (aggregated) TV-means. The noisy images are classical images corrupted with a white Gaussian noise with standard deviation 20. Click on the links in the PSNR table below to see the images.

 
Barbara
Lena
Boats
House
Peppers
noisy
22.1
22.1
22.1
22.1
22.1
TV [1]
26.69
30.89
29.21
31.22
29.62
NL-means [2]
29.59
31.50
29.32
32.05
30.12
TV-means
30.93
32.48
30.00
33.10
30.63


History

february 2010: first version
april 2011: link to revised preprint (to appear in SIIMS)


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