Richardson-Lucy Algorithm With Total Variation Regularization for 3D Confocal Microscope Deconvolution

KEY WORDS image deconvolution; total variation regularization; Poisson noise; fluorescenceconfocal microscopyABSTRACT Confocal laser scanning microscopy is a powerful and popular technique for 3Dimaging of biological specimens. Although confocal microscopy images are much sharper thanstandard epifluorescence ones, they are still degraded by residual out-of-focus light and by Poissonnoise due to photon-limited detection. Several deconvolution methods have been proposed to reducethese degradations, including the Richardson–Lucy iterative algorithm, which computes maximumlikelihood estimation adapted to Poisson statistics. As this algorithm tends to amplify noise, regu-larization constraints based on some prior knowledge on the data have to be applied to stabilize thesolution. Here, we propose to combine the Richardson–Lucy algorithm with a regularization con-straint based on Total Variation, which suppresses unstable oscillations while preserving objectedges. We show on simulated and real images that this constraint improves the deconvolutionresults as compared with the unregularized Richardson–Lucy algorithm, both visually and quanti-tatively. Microsc. Res. Tech. 69:260–266, 2006.