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Deconvolution of 3D Fluorescence Micrographs with Automatic Risk Minimization

S. Ramani, C. Vonesch, M. Unser

Proceedings of the Fifth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'08), Paris, French Republic, May 14-17, 2008, pp. 732-735.

We investigate the problem of automatic tuning of a deconvolution algorithm for three-dimensional (3D) fluorescence microscopy; specifically, the selection of the regularization parameter λ. 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.

AUTHOR="Ramani, S. and Vonesch, C. and Unser, M.",
TITLE="Deconvolution of {3D} Fluorescence Micrographs with Automatic
        Risk Minimization",
BOOKTITLE="Proceedings of the Fifth {IEEE} International Symposium on
        Biomedical Imaging: {F}rom Nano to Macro ({ISBI'08})",
address="Paris, French Republic",
month="May 14-17,",

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