A QUANTITATIVE COMPARISON OF IMAGE-RESTORATION METHODS FOR CONFOCAL MICROSCOPY

In this paper, we compare the performance of three iterative methods f or image restoration: the Richardson-Lucy algorithm, the iterative con strained Tikhonov-Miller algorithm (ICTM) and the Carrington algorithm , Both the ICTM and the Carrington algorithm are based on an additive Gaussian noise model, but differ in the way they incorporate the non-n egativity constraint. Under low light-level conditions, this additive (Gaussian) noise model is a poor description of the actual photon-limi ted image recording, compared with that of a Poisson process. The Rich ardson-Lucy algorithm is a maximum likelihood estimator for the intens ity of a Poisson process, We have studied various methods for determin ing the regularization parameter of the ICTM and the Carrington algori thm and propose a (Gaussian) prefiltering to reduce the noise sensitiv ity of the Richardson-Lucy algorithm. The results of these algorithms are compared on spheres convolved with a point spread function and dis torted by Poisson noise. Our simulations show that the Richardson-Lucy algorithm, with Gaussian prefiltering, produces the best result in mo st of the tests. Finally, we show an example of how restoration method s can improve quantitative analysis: the total amount of fluorescence inside a closed object is measured in the vicinity of another object b efore and after restoration.