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.

• Type: Conference
• Year: 2008
• Where: ISBI
• Authors: Sathish Ramani, Cédric Vonesch, Michael Unser
• Keywords: Gaussian Read-out Noise, ISBI 2008, Medical Imaging, Poisson Photon-counting Noise, Regularization Parameter