Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane-bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.

In general, image sensor noise is dominated by Poisson statistics, even at high illumination level, yet most standard denoising procedures often assume a simpler additive Gaussian noise, which is in fact a poor approximation. Fortunately, Poisson noise can under some circumstances be simplified via variance stabilizing methods, such as the Anscombe transform, which is well known to statisticians, medical imaging specialists and astronomers. However, in order to use such a procedure effectively, the actual photon count needs to be known and not simply an illumination intensity, which is the main reason why such procedures are not frequently used in the image processing community. In this article, we propose to use Poisson distribution characteristics to estimate the photon count from relative illumination data, under simple hypotheses. This allows us to use variance-stabilizing methods on standard digital photographs. Thanks to this, the noise becomes close to additive Gaussian and standard filtering methods become significantly more effective. As an example we exhibit the level of improvement that can be achieved using the bilateral filter.