Abstract
We achieve tomography of 3D volumetric natural objects, where each projected 2D image corresponds to a
different specimen. Each specimen has unknown random
3D orientation, location, and scale. This imaging scenario is relevant to microscopic and mesoscopic organisms, aerosols and hydrosols viewed naturally by a microscope. In-class scale variation inhibits prior single-particle
reconstruction methods. We thus generalize tomographic
recovery to account for all degrees of freedom of a similarity transformation. This enables geometric self-calibration
in imaging of transparent objects. We make the computational load manageable and reach good quality reconstruction in a short time. This enables extraction of statistics
that are important for a scientific study of specimen populations, specifically size distribution parameters. We apply
the method to study of plankton