Scattering effects in images, including those related to haze, fog and appearance of clouds, are fundamentally dictated by microphysical characteristics of the scatterers. This work defines and derives recovery of these characteristics, in a three-dimensional (3D) heterogeneous medium. Recovery is based on a novel tomography approach. Multi-view (multi-angular) and multi-spectral data are linked to the underlying microphysics using 3D radiative transfer, accounting for multiple-scattering. Despite the nonlinearity of the tomography model, inversion is enabled using a few approximations that we describe. As a case study, we focus on passive remote sensing of the atmosphere, where scatterer retrieval can benefit modeling and forecasting of weather, climate and pollution.