On the use of deep learning for three-dimensional computational imaging

Abstract

Deep learning 1 has proven to be an efficient and robust method for many computational imaging systems. 2 The advantages of machine learning, as a rule, are that it is fast-at least in its supervised form after training is complete-and seems exceedingly effective in capturing regularizing priors. Here, we focus the discussion on non-invasive three-dimensional (3D) object reconstruction. One then faces the additional dilemma of choosing the appropriate model of light-matter interaction inside the specimen, i.e. the forward operator. We describe the three stages of approximation that are applicable: weak scattering with weak diffraction (also known as the Radon transform), weak scattering with strong diffraction, and strong scattering. We then overview machine learning approaches for the various models, and glance at the consequences of oversimplifying the forward operator choice.