FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2021738127> ?p ?o ?g. }

• W2021738127 endingPage "1051" @default.
• W2021738127 startingPage "1038" @default.
• W2021738127 abstract "In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models and Laplacian mixture models. These three prior models are studied within the same framework of least-squares optimization. The presented procedures are compared to Gaussian prior model and single-band denoising procedures. We analyze the suppression of non-correlated as well as correlated white Gaussian noise on multispectral and hyperspectral remote sensing data and Rician distributed noise on multiple images of within-modality magnetic resonance data. It is shown that a superior denoising performance is obtained when (a) the interband covariances are fully accounted for and (b) prior models are used that better approximate the marginal distributions of the wavelet coefficients." @default.
• W2021738127 created "2016-06-24" @default.
• W2021738127 creator A5003320138 @default.
• W2021738127 creator A5009310543 @default.
• W2021738127 creator A5060019063 @default.
• W2021738127 creator A5071483672 @default.
• W2021738127 creator A5078974248 @default.
• W2021738127 date "2008-07-01" @default.
• W2021738127 modified "2023-09-26" @default.
• W2021738127 title "Denoising of multicomponent images using wavelet least-squares estimators" @default.
• W2021738127 cites W1496185851 @default.
• W2021738127 cites W1964809923 @default.
• W2021738127 cites W2009995421 @default.
• W2021738127 cites W2037384528 @default.
• W2021738127 cites W2065391104 @default.
• W2021738127 cites W2083394664 @default.
• W2021738127 cites W2106200738 @default.
• W2021738127 cites W2109115094 @default.
• W2021738127 cites W2113945798 @default.
• W2021738127 cites W2120348721 @default.
• W2021738127 cites W2124335859 @default.
• W2021738127 cites W2125527601 @default.
• W2021738127 cites W2129553449 @default.
• W2021738127 cites W2132984323 @default.
• W2021738127 cites W2134929491 @default.
• W2021738127 cites W2136625467 @default.
• W2021738127 cites W2136910298 @default.
• W2021738127 cites W2137733585 @default.
• W2021738127 cites W2140667604 @default.
• W2021738127 cites W2140804420 @default.
• W2021738127 cites W2144451417 @default.
• W2021738127 cites W2144626005 @default.
• W2021738127 cites W2146842127 @default.
• W2021738127 cites W2154688547 @default.
• W2021738127 cites W2160484748 @default.
• W2021738127 cites W2160637922 @default.
• W2021738127 cites W2166632292 @default.
• W2021738127 cites W2170885533 @default.
• W2021738127 cites W4231723990 @default.
• W2021738127 cites W4240321282 @default.
• W2021738127 cites W4255272544 @default.
• W2021738127 cites W4255521522 @default.
• W2021738127 doi "https://doi.org/10.1016/j.imavis.2007.11.003" @default.
• W2021738127 hasPublicationYear "2008" @default.
• W2021738127 type Work @default.
• W2021738127 sameAs 2021738127 @default.
• W2021738127 citedByCount "25" @default.
• W2021738127 countsByYear W20217381272012 @default.
• W2021738127 countsByYear W20217381272013 @default.
• W2021738127 countsByYear W20217381272014 @default.
• W2021738127 countsByYear W20217381272016 @default.
• W2021738127 countsByYear W20217381272018 @default.
• W2021738127 countsByYear W20217381272019 @default.
• W2021738127 countsByYear W20217381272020 @default.
• W2021738127 countsByYear W20217381272023 @default.
• W2021738127 crossrefType "journal-article" @default.
• W2021738127 hasAuthorship W2021738127A5003320138 @default.
• W2021738127 hasAuthorship W2021738127A5009310543 @default.
• W2021738127 hasAuthorship W2021738127A5060019063 @default.
• W2021738127 hasAuthorship W2021738127A5071483672 @default.
• W2021738127 hasAuthorship W2021738127A5078974248 @default.
• W2021738127 hasBestOaLocation W20217381272 @default.
• W2021738127 hasConcept C105795698 @default.
• W2021738127 hasConcept C107673813 @default.
• W2021738127 hasConcept C11413529 @default.
• W2021738127 hasConcept C121332964 @default.
• W2021738127 hasConcept C153180895 @default.
• W2021738127 hasConcept C154945302 @default.
• W2021738127 hasConcept C159078339 @default.
• W2021738127 hasConcept C163294075 @default.
• W2021738127 hasConcept C163716315 @default.
• W2021738127 hasConcept C177769412 @default.
• W2021738127 hasConcept C185429906 @default.
• W2021738127 hasConcept C33923547 @default.
• W2021738127 hasConcept C41008148 @default.
• W2021738127 hasConcept C4199805 @default.
• W2021738127 hasConcept C47432892 @default.
• W2021738127 hasConcept C62520636 @default.
• W2021738127 hasConceptScore W2021738127C105795698 @default.
• W2021738127 hasConceptScore W2021738127C107673813 @default.
• W2021738127 hasConceptScore W2021738127C11413529 @default.
• W2021738127 hasConceptScore W2021738127C121332964 @default.
• W2021738127 hasConceptScore W2021738127C153180895 @default.
• W2021738127 hasConceptScore W2021738127C154945302 @default.
• W2021738127 hasConceptScore W2021738127C159078339 @default.
• W2021738127 hasConceptScore W2021738127C163294075 @default.
• W2021738127 hasConceptScore W2021738127C163716315 @default.
• W2021738127 hasConceptScore W2021738127C177769412 @default.
• W2021738127 hasConceptScore W2021738127C185429906 @default.
• W2021738127 hasConceptScore W2021738127C33923547 @default.
• W2021738127 hasConceptScore W2021738127C41008148 @default.
• W2021738127 hasConceptScore W2021738127C4199805 @default.
• W2021738127 hasConceptScore W2021738127C47432892 @default.
• W2021738127 hasConceptScore W2021738127C62520636 @default.
• W2021738127 hasIssue "7" @default.
• W2021738127 hasLocation W20217381271 @default.
• W2021738127 hasLocation W20217381272 @default.
• W2021738127 hasOpenAccess W2021738127 @default.

• next