Linked Life Data

15646356

Source:http://linkedlifedata.com/resource/pubmed/id/15646356

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2005-1-13
pubmed:abstractText
This paper presents a new impulse noise filter based on pulse coupled neural networks according to the apparent difference of gray value between noised pixels and the pixels around them. Comparing with the state-of-the-art denoised PCNN filter, the step by step modifying algorithm based on PCNN also, the new PCNN filter suggested in this paper costs less computation and less execution time. At the same time this new PCNN filter has been compared with other nonlinear filters, such as median filter, the stack filter based on omnidirectional structural elements constrains, the Omnidirectional morphology Open-Closing maximum filter (OOCmax) and the Omnidirectional morphology Close-Opening minimum (OCOmin) filter. The results of simulation shows that this algorithm is superior to standard median filter, the state-of-the-art PCNN filter, the maximal, minimal morphological filter with omnidirectional structuring elements, and the optimal stack filter based on omnidirectional structural elements constrains in the aspect of the impulse noise removal. What is more important is that this algorithm can keep the details of images more effectively.
pubmed:language
chi
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1001-5515
pubmed:author
pubmed:issnType
Print
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1019-23
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
[A new impulse noise filter based on pulse coupled neural network].
pubmed:affiliation
School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China. ydma@lzu.edu.cn
pubmed:publicationType
Journal Article, English Abstract, Research Support, Non-U.S. Gov't