Single-particle reconstruction in cryo-EM based on three-dimensional weighted nuclear norm minimization
•Firstly, to overcome the over-smooth and the staircase effects of the TV-based reconstruction models, we apply the low rank-based model to better suppress the noise in the SPR task.•Secondly, treating different features the same is not conducive to the use of features, resulting in unsatisfactory r...
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| Vydáno v: | Pattern recognition Ročník 143; s. 109736 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.11.2023
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| ISSN: | 0031-3203 |
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| Abstract | •Firstly, to overcome the over-smooth and the staircase effects of the TV-based reconstruction models, we apply the low rank-based model to better suppress the noise in the SPR task.•Secondly, treating different features the same is not conducive to the use of features, resulting in unsatisfactory reconstruction results. We use the weighted nuclear norm to assign different weights to different features so that the model can make better use of features and reconstruct results with better structure and details.•Thirdly, as the SPR task has a three-dimensional structure, we extend the WNNM model to three-dimension and give the theoretical analysis of the three-dimensional weighted nuclear norm minimization (3DWNNM).•Fourthly, we design an efficient and theoretically guaranteed algorithm for the proposed model, which can solve the model accurately. Experiments on SPR in cryo-EM show that the proposed method can provide more outstanding reconstruction results than several state-of-the-art methods.
Single-particle reconstruction (SPR) in cryogenic electron microscopy (cryo-EM) aims at aligning and averaging two-dimensional micrographs to reconstruct a three-dimensional particle.
How to reconstruct micrographs from heavy noise is a crucial point for achieving better micrograph quality, and thus many methods focus on noise removal. However, new problems such as over-smoothing often occur in their results due to failure in handling heavy noise well. This paper proposes a three-dimensional weighted nuclear norm minimization (3DWNNM) model for SPR in the cryo-EM task to address these issues. Specifically, we design a minimization solver based on the forward-backward splitting algorithm to tackle our model efficiently. Under certain conditions, this solution has an energy-decaying feature and performs exceptionally well in reconstruction. Numerical experiments fully demonstrate the effectiveness and the robustness of the proposed method. |
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| AbstractList | •Firstly, to overcome the over-smooth and the staircase effects of the TV-based reconstruction models, we apply the low rank-based model to better suppress the noise in the SPR task.•Secondly, treating different features the same is not conducive to the use of features, resulting in unsatisfactory reconstruction results. We use the weighted nuclear norm to assign different weights to different features so that the model can make better use of features and reconstruct results with better structure and details.•Thirdly, as the SPR task has a three-dimensional structure, we extend the WNNM model to three-dimension and give the theoretical analysis of the three-dimensional weighted nuclear norm minimization (3DWNNM).•Fourthly, we design an efficient and theoretically guaranteed algorithm for the proposed model, which can solve the model accurately. Experiments on SPR in cryo-EM show that the proposed method can provide more outstanding reconstruction results than several state-of-the-art methods.
Single-particle reconstruction (SPR) in cryogenic electron microscopy (cryo-EM) aims at aligning and averaging two-dimensional micrographs to reconstruct a three-dimensional particle.
How to reconstruct micrographs from heavy noise is a crucial point for achieving better micrograph quality, and thus many methods focus on noise removal. However, new problems such as over-smoothing often occur in their results due to failure in handling heavy noise well. This paper proposes a three-dimensional weighted nuclear norm minimization (3DWNNM) model for SPR in the cryo-EM task to address these issues. Specifically, we design a minimization solver based on the forward-backward splitting algorithm to tackle our model efficiently. Under certain conditions, this solution has an energy-decaying feature and performs exceptionally well in reconstruction. Numerical experiments fully demonstrate the effectiveness and the robustness of the proposed method. |
| ArticleNumber | 109736 |
| Author | Huang, Chaoyan Dong, Bin Wu, Tingting Li, Juncheng Zeng, Tieyong |
| Author_xml | – sequence: 1 givenname: Chaoyan orcidid: 0000-0002-9347-3151 surname: Huang fullname: Huang, Chaoyan organization: Department of Mathematics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong – sequence: 2 givenname: Tingting surname: Wu fullname: Wu, Tingting organization: School of Science, Nanjing University of Posts and Telecommunications, Nanjing, China – sequence: 3 givenname: Juncheng surname: Li fullname: Li, Juncheng organization: School of Communication & Information Engineering, Shanghai University, Shanghai, China – sequence: 4 givenname: Bin surname: Dong fullname: Dong, Bin organization: Beijing International Center for Mathematical Research (BICMR), Center for Machine Learning Research (CMLR), Peking University, China – sequence: 5 givenname: Tieyong orcidid: 0000-0002-0688-202X surname: Zeng fullname: Zeng, Tieyong email: zeng@math.cuhk.edu.hk organization: Department of Mathematics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong |
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| Cites_doi | 10.1016/j.patcog.2022.108537 10.1109/MSP.2019.2957822 10.1016/j.patcog.2019.02.011 10.1080/02331930412331327157 10.1016/j.cam.2019.06.004 10.1080/00207160.2019.1581178 10.1016/j.patcog.2016.12.021 10.1109/TIP.2019.2961429 10.1137/19M1307329 10.1109/TIP.2020.3000349 10.3934/ipi.2019030 10.1016/j.patcog.2022.108914 10.1007/s10107-014-0826-5 10.1109/TIP.2020.2994411 10.1137/15100463X 10.1016/j.patcog.2022.108665 10.1137/18M1202311 10.1016/j.apm.2017.04.002 10.1016/j.patcog.2021.108019 10.1137/130916436 10.1016/j.patcog.2021.108142 10.3934/naco.2013.3.295 10.1137/17M1120439 10.1137/130935434 10.1007/s10915-016-0282-x 10.1137/16M1107863 10.1109/TMI.2021.3130934 10.1109/TMI.2021.3125777 10.1137/050626090 10.1016/j.cam.2019.112680 10.1137/20M1341490 10.1016/j.patcog.2019.107181 10.1007/s10915-020-01344-4 10.1016/j.patcog.2021.107990 10.1137/17M1123237 |
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| Keywords | Single-particle reconstruction Three-dimensional weighted nuclear norm minimization Forward-backward splitting algorithm Cryogenic electron microscopy |
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| SubjectTerms | Cryogenic electron microscopy Forward-backward splitting algorithm Single-particle reconstruction Three-dimensional weighted nuclear norm minimization |
| Title | Single-particle reconstruction in cryo-EM based on three-dimensional weighted nuclear norm minimization |
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