Fast non local means denoising for 3D MR images
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 4191 LNCS - II, Issue: Pt 2, Page: 33-40
2006
- 167Citations
- 113Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations167
- Citation Indexes167
- 167
- CrossRef55
- Captures113
- Readers113
- 113
Conference Paper Description
One critical issue in the context of image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image conspicuity and to improve the performances of all the processings needed for quantitative imaging analysis. The method proposed in this paper is based on an optimized version of the Non Local (NL) Means algorithm. This approach uses the natural redundancy of information in image to remove the noise. Tests were carried out on synthetic datasets and on real 3T MR images. The results show that the NL-means approach outperforms other classical denoising methods, such as Anisotropic Diffusion Filter and Total Variation. © Springer-Verlag Berlin Heidelberg 2006.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84883843392&origin=inward; http://dx.doi.org/10.1007/11866763_5; http://www.ncbi.nlm.nih.gov/pubmed/17354753; http://link.springer.com/10.1007/11866763_5; https://dx.doi.org/10.1007/11866763_5; https://link.springer.com/chapter/10.1007/11866763_5; http://www.springerlink.com/index/10.1007/11866763_5; http://www.springerlink.com/index/pdf/10.1007/11866763_5
Springer Science and Business Media LLC
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