[IPOL announce] new article: Implementation of the "Non-Local Bayes" (NL-Bayes) Image Denoising Algorithm

[announcements about the IPOL journal]

A new preprint is available in IPOL: http://www.ipol.im/pub/art/2013/16/

Marc Lebrun, Antoni Buades, and Jean-Michel Morel,
Implementation of the "Non-Local Bayes" (NL-Bayes) Image Denoising 
Algorithm,
Image Processing On Line, vol. 2013, pp. 1–42.
http://dx.doi.org/10.5201/ipol.2013.16

Abstract:
This article presents a detailed implementation of the Non-Local Bayes 
(NL-Bayes) image denoising algorithm. In a nutshell, NL-Bayes is an 
improved variant of NL-means. In the NL-means algorithm, each patch is 
replaced by a weighted mean of the most similar patches present in a 
neighborhood. Images being mostly self-similar, such instances of 
similar patches are generally found, and averaging them increases the 
SNR. The NL-Bayes strategy improves on NL-means by evaluating for each 
group of similar patches a Gaussian vector model. To each patch is 
therefore associated a mean (which would be the result of NL-means), but 
also a covariance matrix estimating the variability of the patch group. 
This permits to compute an optimal (in the sense of Bayesian minimal 
mean square error) estimate of each noisy patch in the group, by a 
simple matrix inversion.

The implementation proceeds in two identical iterations, but the second 
iteration uses the denoised image of the first iteration to estimate 
better the mean and covariance of the patch Gaussian models. A 
discussion of the algorithm shows that it is close in spirit to several 
state of the art algorithms (TSID, BM3D, BM3D-SAPCA), and that its 
structure is actually close to BM3D. Thorough experimental comparison 
made in this paper also shows that the algorithm achieves the best state 
of the art on color images in terms of PSNR and image quality. On grey 
level images, it reaches a performance similar to the more complex 
BM3D-SAPCA (no color version is available for this last algorithm).