SaID: Simulation-Aware Image Denoising Pre-trained Model for Cryo-EM Micrographs

Cryo-Electron Microscopy (cryo-EM) is a revolutionary technique for determining the structures of proteins and macromolecules. Physical limitations of the imaging conditions cause a very low Signal-to-Noise Ratio (SNR) in cryo-EM micrographs, resulting in difficulties in downstream analysis and accurate ultrastructure determination. Hence, the effective denoising algorithm for cryo-EM micrographs is in demand to facilitate the quality of analysis in macromolecules. However, lacking rich and well-defined dataset with ground truth images, supervised image denoising methods generalize poorly to experimental micrographs. To address this issue, we present a Simulation-aware Image Denoising (SaID) pre-trained model for improving the SNR of cryo-EM micrographs by only training with the accurately simulated dataset. Firstly, we devise a calibration algorithm for the simulation parameters of cryo-EM micrographs to fit the experimental micrographs. Secondly, with the accurately simulated dataset, we propose to train a deep general denoising model which can well generalize to real experimental cryo-EM micrographs. Extensive experimental results demonstrate that our pre-trained denoising model can perform outstandingly on experimental cryo-EM micrographs and simplify the downstream analysis. This indicates that a network only trained with accurately simulated noise patterns can reach the capability as if it had been trained with rich real data. Code and data are available at https://github.com/ZhidongYang/SaID.