DRACO通过自监督学习提升冷冻电镜图像降噪能力，性能优于现有方法。
DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
- 基于奇偶帧分离与噪声重建混合训练，实现无监督图像去噪
- 在27万张微图形数据上预训练，显著提升降噪与粒子检测效果
- 适合作为冷冻电镜通用基础模型，适用于多种下游任务
计算机视觉中的基础模型通过大规模自监督预训练，在零样本和少样本任务中表现出色。然而，这些模型常忽视冷冻电镜（cryo-EM）图像中由高阶噪声带来的严重退化。我们提出DRACO，一种面向冷冻电镜的去噪-重构自编码器，灵感来自Noise2Noise（N2N）方法。通过将冷冻电镜视频拆分为奇数帧和偶数帧，并将其视为独立的噪声观测，采用去噪-重构混合训练策略。对两组图像均进行掩码处理，分别构建去噪与重构任务。预训练依赖高质量数据集，因此我们从非标注公开数据库构建了一个包含超过27万条视频或显微图像的高质量、多样化数据集。预训练后，DRACO可直接作为可泛化的冷冻电镜图像去噪器，也可作为多种冷冻电镜下游任务的基础模型。相比现有最优基线，DRACO在图像去噪、显微图像清理和粒子检测任务中表现最佳。
原文摘要 · Abstract (English)
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.
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