Ramani, SathishVonesch, CedricUnser, Michael2010-11-302010-11-302010-11-30200810.1109/ISBI.2008.4541100https://infoscience.epfl.ch/handle/20.500.14299/61138WOS:000258259800184We investigate the problem of automatic tuning of a deconvolution algorithm for three-dimensional (3D) fluorescence microscopy; specifically, the selection of the regularization parameter lambda. 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.3D fluorescence microscopydeconvolutionunbiased risk estimate (URE)CCD noiseMicroscopytext::conference output::conference proceedings::conference paper