GetDenoisingResults | R Documentation |
GetDenoisingResults
returns the denoised version of
a dynamical image sequence as an array having the same
dimensions as the original sequence.
GetDenoisingResults(data.array, res.listdenois)
data.array |
a (2D or 3D)+T array containing the original dynamic sequence of images (the dataset). The last dimension is the time. |
res.listdenois |
the list resulting from the
|
an array with same dimension as data.array
containing the denoised version.
Tiffany Lieury, Christophe Pouzat, Yves Rozenholc
Rozenholc, Y. and Reiss, M. (2012) Preserving time structures while denoising a dynamical image, Mathematical Methods for Signal and Image Analysis and Representation (Chapter 12), Florack, L. and Duits, R. and Jongbloed, G. and van~Lieshout, M.-C. and Davies, L. Ed., Springer-Verlag, Berlin
Lieury, T. and Pouzat, C. and Rozenholc, Y. (submitted) Spatial denoising and clustering of dynamical image sequence: application to DCE imaging in medicine and calcium imaging in neurons
RunDenoising
## Not run: library(DynClust) ## use fluorescence calcium imaging of neurons performed with Fura 2 excited at 340 nm data('adu340_4small',package='DynClust') ## Gain of the CCD camera: G <- 0.146 ## readout variance of the CCD camera: sro2 <- (16.4)^2 ## Stabilization of the variance to get a normalized dataset (variance=1) FT <- 2*sqrt(adu340_4small/G + sro2) FT.range = range(FT) ## launches the denoising step on the dataset with a statistical level of 5% FT.den.tmp <- RunDenoising(FT,1,mask.size=NA,nproc=2) ## get the results of the denoising step FT.den.res <- GetDenoisingResults(FT,FT.den.tmp) ## plot results at time 50 in same grey scale par(mfrow=c(1,3)) image(FT[,,50],zlim=FT.range,col=gray(seq(0,1,l=128))) title('Original') image(FT.den.res[,,50],zlim=FT.range,col=gray(seq(0,1,l=128))) title('Denoised') image(FT.den.res[,,50]-FT[,,50],col=gray(seq(0,1,l=128))) title('Residuals') ## End(Not run)
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