Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining-Reference-Cited by-同舟云学术

Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining

Published:2024-06-01 Issue:6 Volume:31 Page:564-575
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Yang Zhidong¹²^ORCID,Li Hongjia³,Zang Dawei¹,Han Renmin⁴,Zhang Fa⁵

Affiliation:

1. High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

2. University of Chinese Academy of Sciences, Beijing, China.

3. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA.

4. Research Center for Mathematics and Interdisciplinary Sciences, Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Shandong University, Qingdao, China.

5. School of Medical Technology, Beijing Institute of Technology, Beijing, China.

Publisher

Mary Ann Liebert Inc

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2024.0513

Reference28 articles.

1. Topaz-Denoise: general deep denoising models for cryoEM and cryoET

2. Buades A, Coll B, Morel J-M. A Non-Local Algorithm for Image Denoising. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE: 2005; pp. 60–65.

3. 2.8 Å resolution reconstruction of the Thermoplasma acidophilum 20S proteasome using cryo-electron microscopy

4. Chen J, Chen J, Chao H, et al. Image Blind Denoising with Generative Adversarial Network Based Noise Modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE: 2018; pp. 3155–3164.

5. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering