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A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video

Overview of attention for article published in ADS, December 2007
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Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
165 Dimensions

Readers on

mendeley
54 Mendeley
Title
A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video
Published in
ADS, December 2007
DOI 10.1155/2007/74585
Authors

Michael K. Ng, Huanfeng Shen, Edmund Y. Lam, Liangpei Zhang

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Sweden 1 2%
Brazil 1 2%
Unknown 50 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 33%
Researcher 7 13%
Student > Doctoral Student 7 13%
Student > Master 4 7%
Student > Bachelor 3 6%
Other 9 17%
Unknown 6 11%
Readers by discipline Count As %
Engineering 22 41%
Computer Science 18 33%
Earth and Planetary Sciences 2 4%
Biochemistry, Genetics and Molecular Biology 1 2%
Mathematics 1 2%
Other 3 6%
Unknown 7 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 March 2014.
All research outputs
#8,535,684
of 25,377,790 outputs
Outputs from ADS
#7,327
of 25,979 outputs
Outputs of similar age
#43,544
of 166,856 outputs
Outputs of similar age from ADS
#126
of 375 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 25,979 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 166,856 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 375 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.