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.
Vicidomini
Giuseppe
G
LAMBS-MicroSCoBiO, DISI, University of Genoa, Genoa, Italy.
Mondal
Partha Pratim
PP
Diaspro
Alberto
A
D000465:Algorithms; D001185:Artificial Intelligence; D017143:Fuzzy Logic; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*