English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The Non-Local Means (NLM) image denoising algorithm pushed the limits ofdenoising. But it introduced a new paradigm, according to which one couldcapture the similarity of images with the NLM weights. We show that, contraryto the prevailing opinion, the NLM weights do not allow to get a reliablemeasure of the similarity in a noisy image, unless one add a localityconstraint. As an image denoising method, the Non-Local Means prove to belocal. Some works had already pointed out that to get the best denoisingperformances with the NLM algorithm, one should run it locally. But no generalconclusion has been yet proposed and the only explanation that was proposed tojustify the experimental results is not sufficient. Our study based onexperimental evidence proves that, on average on natural images, the bias ofthe NLM estimator is an increasing function of the radius of the similaritysearching zone. The reason for this phenomenon is that noise disrupts the orderof similarity between patches. Hence the mean squared error between theoriginal image and the NLM estimation, which is the sum of the bias, thevariance and the covariance of the estimator, has an absolute minimum for adisk of radius 3 to 4 pixels.

Non-Local means est un algorithme de d\'ebruitage local (Non-Local means is a local image denoising algorithm)