Three-dimensional photon counting microscopy using Bayesian estimation

Abstract

We present three-dimensional photon counting microscopy using Bayesian estimation. To record the light intensity information of objects in photon-starved conditions, photon counting imaging can be used. In conventional photon counting imaging, maximum likelihood estimation (MLE) or Bayesian estimation with uniform statistical parameters has been used for 3D visualization. Since MLE does not use the prior information of the estimated target, its visual quality is not enough to recognize 3D microorganisms when low number of photons is used. In addition, because Bayesian estimation with uniform statistical parameters uses fixed statistical parameters over the whole image, the estimated image seems to be image with boost-up light intensity. On the other hand, our proposed method uses the nonuniform statistical parameters for prior information of microorganisms to estimate 3D profile of them. Therefore, this method may enhance the visual quality of 3D microscopy results with low number of photons.