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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The topic of denoising magnetic resonance (MR) images is considered in this paper. More in detail, an enhanced Non-Local Means (NLM) filter using the Kolmogorov-Smirnov (KS) distance is proposed. The KS-NLM approach estimates the similarity between image patches by computing the KS distance. To overcome that NLM filters assign the same role to all pixels in patches, that is, not privileging the central one, we propose a new filter, namely the Anisotropic Weighted KS-NLM (Aw KS-NLM), which better deals with central pixels within the patches by, on one hand, including a suitable weighted strategy and, on the other, by performing a local anisotropy analysis. The Aw KS-NLM has been compared to other existing non-local Means (NLM) methodologies in both MRI simulated and real datasets. The results provide excellent noise reduction and image-detail preservation.

Anisotropic Weighted KS-NLM Filter for Noise Reduction in MRI