A statistical interpretation of non-local means

N. A. Thacker, J. V. Manjon, P. A. Bromiley

    Research output: Contribution to conferencePoster


    Noise filtering is a common step in image processing, and is particularly effective in improving the subjective quality of images. A large number of techniques have been developed, many of which concentrate on the problem of removing noise without damaging small structures such as edges. One recent approach that demonstrates empirical merit is the non-local means (NLM) algorithm. With the increasing use of imaging in medicine and sciences it might be considered inevitable that researchers will try to apply such noise filtering schemes in quantitative analysis. In order to do this with confidence we need to develop an understanding of the noise removal process that goes beyond subjective appearance. The purpose of this paper is to develop and test a statistical basis of NLM, in order to try to understand the conditions required for its use. The theory is illustrated on synthetic data and real MR images of the brain. ©2008 The Institution of Engineering and Technology.
    Original languageEnglish
    Number of pages5
    Publication statusPublished - 2008
    Event5th International Conference on Visual Information Engineering, VIE 2008 - Xi'an
    Duration: 1 Jul 2008 → …


    Conference5th International Conference on Visual Information Engineering, VIE 2008
    Period1/07/08 → …


    • Noise filtering
    • Quantitative
    • Statistical


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