GetDenoisingResults: Get denoising step result

Description

GetDenoisingResults returns the denoised version of a dynamical image sequence as an array having the same dimensions as the original sequence.

Usage

GetDenoisingResults(data.array, res.listdenois)

Arguments

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 RunDenoising procedure applied to data.array. This parameter may be replaced by the component info.den of the former.

Value

an array with same dimension as data.array containing the denoised version.

References

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

Examples

Run this code

# NOT RUN {
# }
# 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')
# }