A Supervised Multi-task Framework for Joint cryo-ET Restoration Enabled by Generative Physical Simulation

Cryo-electron tomography (cryo-ET) enables in-situ visualization of cellular ultrastructure, but reconstructions are severely degraded by extremely low SNR and missingwedge artifacts due to dose limits and restricted tilt angles. Existing learning-based approaches are further constrained by inaccurate noise modeling and the lack of reliable ground truth, limiting restoration quality. We propose cryoDeRec, a multi-task framework that jointly performs denoising and missing wedge recovery in a fully supervised manner. Our key contribution is a dual-objective training strategy that leverages synthetic noisy tomograms and their corresponding clean tomograms, encouraging structural fidelity while recovering missing information. To support training, we introduce an imaging simulation pipeline that captures authentic noise distributions and incorporates isotropic structural priors by simulating tomograms from real EMDB structures. Experiments on four realistic cryo-ET datasets and two extremely low-SNR simulated datasets (all reconstructed via WBP) show that cryoDeRec restores high-quality tomograms directly from raw inputs without preprocessing, consistently outperforming prior state of the art. Our findings show that training on a comprehensive simulated dataset, which captures realistic noise and structure, enables models to generalize effectively to real cryo-ET tomograms.