Matches in SemOpenAlex for { <https://semopenalex.org/work/W2020105900> ?p ?o ?g. }
Showing items 1 to 62 of
62
with 100 items per page.
- W2020105900 endingPage "1065" @default.
- W2020105900 startingPage "1057" @default.
- W2020105900 abstract "Aiming at the long running time problem of the super-resolution image reconstruction method based on learning, this paper proposes a novel fast method. Principal Component Analysis (PCA) is used to reduce the data dimensionality of training data set and Vector Quantization (VQ) is introduced into super-resolution image reconstruction to divide subset. Both accelerate running of the method speed and solve the long running time problem caused by large amount of training data. In order to ensure the quality of the output image, Stationary Wavelet Transform (SWT) is used to extract the low and high frequency information of the sample image. And Markov Network is improved to nd the best candidate block. Experiments show that without sacricing the quality of nal output high resolution image, the execution speed of the proposed method is greatly improved." @default.
- W2020105900 created "2016-06-24" @default.
- W2020105900 creator A5005685127 @default.
- W2020105900 creator A5013485575 @default.
- W2020105900 creator A5041158035 @default.
- W2020105900 creator A5057400390 @default.
- W2020105900 creator A5073727134 @default.
- W2020105900 date "2014-03-01" @default.
- W2020105900 modified "2023-09-27" @default.
- W2020105900 title "A Fast Super-resolution Image Reconstruction Method Based on Learning" @default.
- W2020105900 cites W1484228140 @default.
- W2020105900 cites W1653130573 @default.
- W2020105900 cites W2097074225 @default.
- W2020105900 cites W2134383396 @default.
- W2020105900 cites W2292976057 @default.
- W2020105900 cites W2358400126 @default.
- W2020105900 cites W2042697733 @default.
- W2020105900 cites W2140257560 @default.
- W2020105900 doi "https://doi.org/10.12733/jics20102875" @default.
- W2020105900 hasPublicationYear "2014" @default.
- W2020105900 type Work @default.
- W2020105900 sameAs 2020105900 @default.
- W2020105900 citedByCount "0" @default.
- W2020105900 crossrefType "journal-article" @default.
- W2020105900 hasAuthorship W2020105900A5005685127 @default.
- W2020105900 hasAuthorship W2020105900A5013485575 @default.
- W2020105900 hasAuthorship W2020105900A5041158035 @default.
- W2020105900 hasAuthorship W2020105900A5057400390 @default.
- W2020105900 hasAuthorship W2020105900A5073727134 @default.
- W2020105900 hasConcept C11413529 @default.
- W2020105900 hasConcept C115961682 @default.
- W2020105900 hasConcept C138268822 @default.
- W2020105900 hasConcept C154945302 @default.
- W2020105900 hasConcept C31972630 @default.
- W2020105900 hasConcept C41008148 @default.
- W2020105900 hasConceptScore W2020105900C11413529 @default.
- W2020105900 hasConceptScore W2020105900C115961682 @default.
- W2020105900 hasConceptScore W2020105900C138268822 @default.
- W2020105900 hasConceptScore W2020105900C154945302 @default.
- W2020105900 hasConceptScore W2020105900C31972630 @default.
- W2020105900 hasConceptScore W2020105900C41008148 @default.
- W2020105900 hasIssue "4" @default.
- W2020105900 hasLocation W20201059001 @default.
- W2020105900 hasOpenAccess W2020105900 @default.
- W2020105900 hasPrimaryLocation W20201059001 @default.
- W2020105900 hasRelatedWork W1533292911 @default.
- W2020105900 hasRelatedWork W2005185696 @default.
- W2020105900 hasRelatedWork W2092957489 @default.
- W2020105900 hasRelatedWork W2130228941 @default.
- W2020105900 hasRelatedWork W2132132164 @default.
- W2020105900 hasRelatedWork W2161229648 @default.
- W2020105900 hasRelatedWork W2235753890 @default.
- W2020105900 hasRelatedWork W2366116130 @default.
- W2020105900 hasRelatedWork W2889893736 @default.
- W2020105900 hasRelatedWork W2993674027 @default.
- W2020105900 hasVolume "11" @default.
- W2020105900 isParatext "false" @default.
- W2020105900 isRetracted "false" @default.
- W2020105900 magId "2020105900" @default.
- W2020105900 workType "article" @default.