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
apply Total-Variation regularization to all image patches
at all scales
for each pixel, select the minimum scale ensuring that a
sufficient number of patches similar to the current patch are found
average all these patches to obtain a "denoised patch"
aggregate all image estimates coming from these
denoised patches to compute the denoised image
This algorithm combines two famous (and very different) image denoising
methods: Total Variation denoising  and NL-means denoising .
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.
 L. Rudin, S. Osher, E. Fatemi,
Nonlinear total variation based noise removal algorithms,
Physica D, vol. 60, n. 1-4, pp. 259-268, 1992.
 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 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.