Linked Life Data

21118774

Source:http://linkedlifedata.com/resource/pubmed/id/21118774

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2011-5-19
pubmed:abstractText
In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures. We model the gradient profiles by a parametric gradient profile model. Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which are called gradient profile prior. Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results. The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1941-0042
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1529-42
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Gradient profile prior and its applications in image super-resolution and enhancement.
pubmed:affiliation
School of Science, Xi’an Jiaotong University, Xi’an 710049, China. jiansun@mail.xjtu.edu.cn
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't