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

Overview of attention for article published in EURASIP Journal on Advances in Signal Processing, June 2007
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Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
157 Dimensions

Readers on

mendeley
53 Mendeley
Title
A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video
Published in
EURASIP Journal on Advances in Signal Processing, June 2007
DOI 10.1155/2007/74585
Authors

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

Mendeley readers

The data shown below were compiled from readership statistics for 53 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 49 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 34%
Researcher 7 13%
Student > Doctoral Student 7 13%
Student > Master 4 8%
Student > Bachelor 3 6%
Other 9 17%
Unknown 5 9%
Readers by discipline Count As %
Engineering 22 42%
Computer Science 18 34%
Earth and Planetary Sciences 2 4%
Biochemistry, Genetics and Molecular Biology 1 2%
Mathematics 1 2%
Other 3 6%
Unknown 6 11%

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
#7,453,126
of 22,785,242 outputs
Outputs from EURASIP Journal on Advances in Signal Processing
#92
of 441 outputs
Outputs of similar age
#24,567
of 68,389 outputs
Outputs of similar age from EURASIP Journal on Advances in Signal Processing
#4
of 6 outputs
Altmetric has tracked 22,785,242 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 441 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 45th percentile – i.e., 45% 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 68,389 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.