http://3demmethods.i2pc.es/index.php?title=2021Fan_Denoising&feed=atom&action=history2021Fan Denoising - Revision history2024-03-29T10:06:38ZRevision history for this page on the wikiMediaWiki 1.29.1http://3demmethods.i2pc.es/index.php?title=2021Fan_Denoising&diff=4064&oldid=prevWikiSysop: Created page with "== Citation == Fan, Y. & Zhao, Z. Cryo-Electron Microscopy Image Denoising Using Multi-Frequency Vector Diffusion Maps. 2021 IEEE Intl. Conf. on Image Processing (ICIP),..."2021-09-17T06:25:36Z<p>Created page with "== Citation == Fan, Y. & Zhao, Z. Cryo-Electron Microscopy Image Denoising Using Multi-Frequency Vector Diffusion Maps. 2021 IEEE Intl. Conf. on Image Processing (ICIP),..."</p>
<p><b>New page</b></p><div>== Citation ==<br />
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Fan, Y. &amp; Zhao, Z. Cryo-Electron Microscopy Image Denoising Using Multi-Frequency Vector Diffusion Maps. 2021 IEEE Intl. Conf. on Image Processing (ICIP), 2021, 3463-3467 <br />
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== Abstract ==<br />
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Cryo-electron microscopy (EM) single particle reconstruction is a general technique for 3D structure determination of macromolecules. However, because the images are taken at low electron dose, it is extremely hard to visualize the individual particle with low contrast and high noise level. In this paper, we propose a novel framework for cryo-EM single particle image denoising, which incorporates the recently developed multi-frequency vector diffusion maps [1] for improving the identification and alignment of images with similar viewing directions. In addition, we propose a novel filtering scheme combining graph signal processing and truncated Fourier-Bessel expansion of the projection images. Through both simulated and publicly available real data, we demonstrate that our proposed method is efficient and robust to noise compared with the state-of-the-art cryo-EM 2D class averaging algorithms. <br />
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== Keywords ==<br />
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== Links ==<br />
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https://ieeexplore.ieee.org/abstract/document/9506435<br />
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== Related software ==<br />
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== Comments ==</div>WikiSysop