Fuzzy logic and maximum a posteriori-based image restoration for confocal microscopy.

We propose a maximum a posteriori image restoration approach to 3D confocal microscopy. The image field is suitably modeled as a Markov random field, resulting in a Gibbs distributed image. A fuzzy-logic-based potential is employed in the Gibbs prior. Unlike other potentials, the fuzzy potential distinguishes intensity variation due to genuine edges and noise. The proposed approach has generated artifact-free restored confocal microscopy images.