Improvement of the LLS and MAP deconvolution algorithms by automatic determination of optimal regularization parameters and pre-filtering of original data

We show that automatic determination of regularization threshold and pre-filtering of 3-D fluorescence microscopic images improves the stability of deconvolution results when using the Linear Least squares Solution or the Maximum a Posteriori method. Doing so, the choice of the regularization parameter much less depends on a priori knowledge of the specimen or skills of the operator. This increases the reliability and repeatability of quantitative measurements on deconvolved images.