Cryo-electron microscopy (EM) single particle reconstruction is an entirely general technique for 3D structure determination of macromolecular complexes. However, because the images are taken at low electron dose, it is extremely hard to visualize the individual particle with low contrast and high noise level. In this paper, we propose a novel approach called multi-frequency vector diffusion maps (MFVDM) to improve the efficiency and accuracy of cryo-EM 2D image classification and denoising. This framework incorporates different irreducible representations of the estimated alignment between similar images. In addition, we propose a graph filtering scheme to denoise the images using the eigenvalues and eigenvectors of the MFVDM matrices. Through both simulated and publicly available real data, we demonstrate that our proposed method is efficient and robust to noise compared with the state-of-the-art cryo-EM 2D class averaging and image restoration algorithms.
翻译:冷冻-电子显微镜(EM)单粒子重建是确定大型分子复合体的3D结构的一种完全普通的技术。 但是,由于图像是在低电子剂量下拍摄的,因此很难以低对比度和高噪音水平来想象单个粒子。 在本文中,我们提议了一种叫作多频矢量扩散图(MFVDM)的新颖方法,以提高冷冻-EM 2D图像分类和脱网的效率和准确性。这个框架包含了对类似图像之间估计对齐的不同不可减损的表示。 此外,我们提议了一个图形过滤方案,用微电子量和微量微量分振荡矩阵的仪和导体将图像密封。通过模拟和公开提供的真实数据,我们证明我们所提议的方法与最先进的冷冻-EM 2D类平均和图像恢复算法相比,对噪音是有效和有力的。