A comparison of image restoration approaches applied to three-dimensional confocal and wide-field fluorescence microscopy

We have compared different image restoration approaches for fluorescence mi croscopy. The most widely used algorithms were classified with a Bayesian t heory according to the assumed noise model and the type of regularization i mposed. We considered both Gaussian and Poisson models for the noise in com bination with Tikhonov regularization, entropy regularization, Good's rough ness and without regularization (maximum likelihood estimation). Simulation s of fluorescence confocal imaging were used to examine the different noise models and regularization approaches using the mean squared error criterio n. The assumption of a Gaussian noise model yielded only slightly higher er rors than the Poisson model. Good's roughness was the best choice for the r egularization. Furthermore, we compared simulated confocal and wide-field d ata. In general, restored confocal data are superior to restored wide-field data, but given sufficient higher signal level for the wide-field data the restoration result may rival confocal data in quality. Finally, a visual c omparison of experimental confocal and wide-field data is presented.