Open AccessNoise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer
Author(s) -
Hongjia Li,
Hui Zhang,
Xiaohua Wan,
Zhidong Yang,
Chengmin Li,
Jintao Li,
Han Ren,
Ping Zhu,
Fa Zhang
Publication year - 2022
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btac052
Subject(s) - noise reduction , noise (video) , computer science , artificial intelligence , pattern recognition (psychology) , noise measurement , signal to noise ratio (imaging) , multiplicative noise , computer vision , image (mathematics) , transmission (telecommunications) , signal transfer function , telecommunications , analog signal
Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods.