Optimizing sampling for surface localization in 3D-scanning microscopy
3D-scanning fluorescence imaging of living tissue is in demand for less phototoxic acquisition process. For the imaging of biological surfaces, adaptive and sparse scanning schemes have been proven to efficiently reduce the light dose by concentrating acquisitions around the surface. In this paper, we focus on optimizing the scanning scheme at constant photon budget, when the problem is to estimate the position of a biological surface whose intensity profile is modeled as a Gaussian shape. We propose an approach based on the Cramer-Rao Bound to optimize the positions and number of scanning points, assuming signal-dependant Gaussian noise. We show that the optimization problem can be reduced to a few parameters, allowing to define quasi-optimal acquisition strategies, first when no prior knowledge on the surface location is available, and then when the user has a prior on this location.