Generative Adversarial Networks for Robust Cryo-EM Image Denoising

Gu, Hanlin; Xian, Yin; Unarta, Ilona Christy; Yao, Yuan

doi:10.1007/978-3-030-98661-2_126

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…The existing mathematical methods for denoising natural images include total variation (TV)-based variants ( Pang et al., 2020 ), block matching and 3D collaborative filtering (BM3D) ( Maggioni et al., 2013 ), k-singular value decomposition (k-SVD) ( Aharon et al., 2005 ), and traditional Wiener filter (TWF). However, the types and levels of noise in the tomography of cryo-ET are different from those of natural images, which cannot obtain a satisfying result on tomographic projections ( Gu et al., 2022 ). The imaging principle of cryo-EM and cryo-ET is the same.…”

Section: D and 3d Denoisersmentioning

confidence: 99%

“…The generator and discriminator are neural networks that run in competition during training. In 2018, the β −GAN approach with l 1 and l 2 loss ( Gu et al., 2022 ) was proposed for denoising 2D cryo-EM images. To overcome the lack of ground truths, Noise-Transfer2Clean (NT2C) ( Li et al., 2022a ) applies the simulation software InSilicoTEM ( Vulović et al., 2013 ) to produce simulated 2D projection images in cryo-EM.…”

Section: D and 3d Denoisersmentioning

confidence: 99%

See 1 more Smart Citation

Computational methods for in situ structural studies with cryogenic electron tomography

Zhao,

Lu,

Zhao

et al. 2023

Front. Cell. Infect. Microbiol.

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Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in situ in terms of further analyzing the working mechanisms of viruses and drug exploitation, among others. A data processing workflow for cryo-ET has been developed to reconstruct three-dimensional density maps and further build atomic models from a tilt series of two-dimensional projections. Low signal-to-noise ratio (SNR) and missing wedge are two major factors that make the reconstruction procedure challenging. Because only few near-atomic resolution structures have been reconstructed in cryo-ET, there is still much room to design new approaches to improve universal reconstruction resolutions. This review summarizes classical mathematical models and deep learning methods among general reconstruction steps. Moreover, we also discuss current limitations and prospects. This review can provide software and methods for each step of the entire procedure from tilt series by cryo-ET to 3D atomic structures. In addition, it can also help more experts in various fields comprehend a recent research trend in cryo-ET. Furthermore, we hope that more researchers can collaborate in developing computational methods and mathematical models for high-resolution three-dimensional structures from cryo-ET datasets.

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Section: D and 3d Denoisersmentioning

confidence: 99%

Section: D and 3d Denoisersmentioning

confidence: 99%

Computational methods for in situ structural studies with cryogenic electron tomography

Zhao,

Lu,

Zhao

et al. 2023

Front. Cell. Infect. Microbiol.

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Robust Single-Particle Cryo-Em Image Denoising and Restoration

Zhang,

Zhao,

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Variance-Reduced Randomized Kaczmarz Algorithm In Xfel Single-Particle Imaging Phase Retrieval

Xian

Liu

Tai

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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In this paper, we propose the Variance Reduced Randomized Kaczmarz (VR-RK) algorithm for XFEL signal particle imaging phase retrieval. The VR-RK algorithm is inspired by the randomized Kaczmarz algorithm and the variance reduction in stochastic gradient methods. The formulations of the VR-RK algorithm under the L 1 and L 2 constraints are also presented. Numerical simulations demonstrate that the VR-RK method has a faster convergence rate compared with the randomized Kaczmarz method. Tests on the synthetic signal particle imaging data and the PR772 XFEL real imaging data show that the VR-RK algorithm can recover information with higher accuracy. It is useful for biological data processing.

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Generative Adversarial Networks for Robust Cryo-EM Image Denoising

Cited by 3 publications

References 38 publications

Computational methods for in situ structural studies with cryogenic electron tomography

Computational methods for in situ structural studies with cryogenic electron tomography

Robust Single-Particle Cryo-Em Image Denoising and Restoration

Variance-Reduced Randomized Kaczmarz Algorithm In Xfel Single-Particle Imaging Phase Retrieval

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