DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
This work addresses noise issues in cryo-EM imaging, a domain-specific problem for structural biology, with incremental improvements over existing methods.
The paper tackled the problem of severe noise corruption in cryogenic electron microscopy (cryo-EM) images by introducing DRACO, a denoising-reconstruction autoencoder, which achieved state-of-the-art performance in denoising, micrograph curation, and particle picking tasks.
Foundation models in computer vision have demonstrated exceptional performance in zero-shot and few-shot tasks by extracting multi-purpose features from large-scale datasets through self-supervised pre-training methods. However, these models often overlook the severe corruption in cryogenic electron microscopy (cryo-EM) images by high-level noises. We introduce DRACO, a Denoising-Reconstruction Autoencoder for CryO-EM, inspired by the Noise2Noise (N2N) approach. By processing cryo-EM movies into odd and even images and treating them as independent noisy observations, we apply a denoising-reconstruction hybrid training scheme. We mask both images to create denoising and reconstruction tasks. For DRACO's pre-training, the quality of the dataset is essential, we hence build a high-quality, diverse dataset from an uncurated public database, including over 270,000 movies or micrographs. After pre-training, DRACO naturally serves as a generalizable cryo-EM image denoiser and a foundation model for various cryo-EM downstream tasks. DRACO demonstrates the best performance in denoising, micrograph curation, and particle picking tasks compared to state-of-the-art baselines.