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2008
IEEE

Deconvolution of 3D fluorescence micrographs with automatic risk minimization

10 years 3 months ago
Deconvolution of 3D fluorescence micrographs with automatic risk minimization
We investigate the problem of automatic tuning of a deconvolution algorithm for three-dimensional (3D) fluorescence microscopy; specifically, the selection of the regularization parameter . For this, we consider a realistic noise model for data obtained from a CCD detector: Poisson photon-counting noise plus Gaussian read-out noise. Based on this model, we develop a new risk measure which unbiasedly estimates the original mean-squared-error of the deconvolved signal estimate. We then show how to use this risk estimate to optimize the regularization parameter for Tikhonov-type deconvolution algorithms. We present experimental results on simulated data and numerically demonstrate the validity of the proposed risk measure. We also present results for real 3D microscopy data.
Sathish Ramani, Cédric Vonesch, Michael Uns
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2008
Where ISBI
Authors Sathish Ramani, Cédric Vonesch, Michael Unser
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