Biomedical Imaging GroupSTI
English only   BIG > Publications > Nonlocal Means

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

Nonlocal Means with Dimensionality Reduction and SURE-Based Parameter Selection

D. Van De Ville, M. Kocher

IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2683-2690, September 2011.

Nonlocal means (NLM) is an effective denoising method that applies adaptive averaging based on similarity between neighborhoods in the image. An attractive way to both improve and speed-up NLM is by first performing a linear projection of the neighborhood. One particular example is to use principal components analysis (PCA) to perform dimensionality reduction. Here, we derive Stein's unbiased risk estimate (SURE) for NLM with linear projection of the neighborhoods. The SURE can then be used to optimize the parameters by a search algorithm or we can consider a linear expansion of multiple NLMs, each with a fixed parameter set, for which the optimal weights can be found by solving a linear system of equations. The experimental results demonstrate the accuracy of the SURE and its successful application to tune the parameters for NLM.

AUTHOR="Van De Ville, D. and Kocher, M.",
TITLE="Nonlocal Means with Dimensionality Reduction and {SURE}-Based
        Parameter Selection",
JOURNAL="{IEEE} Transactions on Image Processing",

© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.