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 structures at near-native state, yet its practical utility is often hampered by extremely low signal-to-noise ratio (SNR) and severe missing wedge artifacts resulting from dose limitations and restricted tilt angles. While several computational methods have been proposed for reconstructing high-quality tomograms, the performance is still limited by the absence of accurate noise modeling and reliable ground truth data. To address this challenge, we propose cryoDeRec, a multi-task learning framework to jointly address denoising and missing wedge reconstruction in fully supervised manner. The main contribution of cryoDeRec is a dual-objective training strategy incorporated with synthetically corrupted tomogram and raw noisy tomogram, enabling simultaneous restoration of structural fidelity and reconstruction of missing information. The model is trained on a physically synthetic dataset generated by a novel imaging simulation pipeline that incorporates authentic noise distributions and isotropic structural priors. We evaluate cryoDeRec on four realistic cryo-ET datasets and two simulated datasets with extremely low SNR, all reconstructed using Weighted Back Projection (WBP). Extensive experimental results demonstrate that our method achieves high-quality restoration directly from raw tomograms without any pre-processing, outperforming existing state-of-the-art methods. Our findings show that training on a comprehensive simulated dataset, which captures realistic noise and structural diversity, enables models to generalize effectively to real cryo-ET tomograms. The code and datasets will be available upon acceptance.