A Dual Model for Restoring Images Corrupted by Mixture of Additive and Multiplicative Noise

Zhao, Cuicui; Li, Jun; Zhang, Jie

doi:10.1109/access.2021.3137995

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…However, as far as we know, few methods are designed for cryo-ET reconstruction by combining mathematical theory and AI algorithms to complement each other. These kinds of approaches have been used for natural image processing ( Wang et al., 2020 ; Zhao et al., 2021 ; Li et al., 2022b ). The MoAMN method ( Zhao et al., 2021 ) combined the EM algorithm and a denoiser for Gaussian noise to remove a mixture of additive and multiplicative noise.…”

Section: Discussionmentioning

confidence: 99%

“…These kinds of approaches have been used for natural image processing (Wang et al, 2020;Zhao et al, 2021;Li et al, 2022b). The MoAMN method (Zhao et al, 2021) combined the EM algorithm and a denoiser for Gaussian noise to remove a mixture of additive and multiplicative noise. A learnable regularizer provides a good image prior to denoising images in Li et al (2022b) by integrating the variational method into the architecture of denoiser.…”

Section: Discussionmentioning

confidence: 99%

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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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