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000195275 005__ 20190617200716.0
000195275 020__ $$a978-1-4673-6455-3
000195275 0247_ $$2doi$$a10.1109/ISBI.2013.6556630
000195275 02470 $$2ISI$$a000326900100235
000195275 037__ $$aCONF
000195275 245__ $$aLinear Interpolation Of Biomedical Images Using A Data-Adaptive Kernel
000195275 269__ $$a2013
000195275 260__ $$bIEEE$$c2013$$aNew York
000195275 300__ $$a4
000195275 336__ $$aConference Papers
000195275 490__ $$aIEEE International Symposium on Biomedical Imaging
000195275 520__ $$aIn this work, we propose a continuous-domain stochastic model that can be applied to image data. This model is autoregressive, and accounts for Gaussian-type as well as for non-Gaussian-type innovations. In order to estimate the corresponding parameters from the data, we introduce two possible error criteria; namely, Gaussian maximum-likelihood, and least-squares autocorrelation fit. Exploiting the link between autoregressive models and spline approximation, we use our approach to adapt interpolation parameters to a given image. Our numerical results demonstrate that our adaptive approach yields higher SNR values compared to classical polynomial splines for the task of image scaling. They also indicate that our least-squares-based error criterion nearly achieves the oracle performance for parameter estimation, which provides further support to the practical relevance of our model.
000195275 6531_ $$aExponential splines
000195275 6531_ $$aimage interpolation
000195275 6531_ $$astochastic modeling
000195275 700__ $$0242489$$g194014$$aKirshner, Hagai
000195275 700__ $$aBourquard, Aurelien
000195275 700__ $$0245475$$g213487$$aWard, John Paul
000195275 700__ $$aUnser, Michael$$g115227$$0240182
000195275 7112_ $$dAPR 07-11, 2013$$cSan Francisco, CA$$aIEEE 10th International Symposium on Biomedical Imaging - From Nano to Macro (ISBI)
000195275 773__ $$t2013 IEEE 10th International Symposium On Biomedical Imaging (ISBI)$$q938-941
000195275 8564_ $$uhttp://bigwww.epfl.ch/publications/kirshner1303.html$$zURL
000195275 8564_ $$uhttp://bigwww.epfl.ch/publications/kirshner1303.pdf$$zURL
000195275 8564_ $$uhttp://bigwww.epfl.ch/publications/kirshner1303.ps$$zURL
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000195275 909CO $$ooai:infoscience.tind.io:195275$$qGLOBAL_SET$$pconf$$pSTI
000195275 917Z8 $$x148230
000195275 937__ $$aEPFL-CONF-195275
000195275 970__ $$akirshner1303/LIB
000195275 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000195275 980__ $$aCONF