Abstract

Three-dimensional microscopy by computational deconvolution methods requires accurate knowledge of the point spread function (PSF) that characterizes the microscope. Experimental PSF's can only be measured over small regions about focus because the small objects necessary for PSF measurement are dim. Theoretical computation of the PSF requires accurate knowledge of all the experimental setup parameters. Some parameters may be difficult or impossible to measure. In blind deconvolution, the PSF and the specimen are estimated simultaneously, an under-determined problem with non-unique solutions. Most existing approaches to blind deconvolution rely on enforcing constraints on the specimen function and PSF, sometimes in ad-hoc ways. We derived a parametric blind deconvolution method by assuming that the PSF follows a mathematical expression with unknown parameters. The parameters are then estimated together with the specimen function. Preliminary results presented here show that this algorithm rapidly estimates the correct PSF.

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