Score-Based Matching with Target Guidance for Cryo-EM Denoising
Xiaoqi Wu, Xueying Zhan, Wen Li, Junhao Wu, Xin Huang, Min Xu

TL;DR
This paper introduces a score-based cryo-EM denoising method that enhances structural preservation and downstream analysis, especially under weak signal conditions.
Contribution
It proposes a novel score-based framework with target guidance for cryo-EM denoising, improving structural consistency over existing pixel-wise methods.
Findings
Improved particle picking accuracy in cryo-EM datasets.
More structure-consistent 3D reconstructions achieved.
Better suppression of background noise enhances downstream analysis.
Abstract
Cryo-electron microscopy (cryo-EM) enables single-particle analysis of biological macromolecules under strict low-dose imaging conditions, but the resulting micrographs often exhibit extremely low signal-to-noise ratios and weak particle visibility. Image denoising is therefore an important preprocessing step for downstream cryo-EM analysis, including particle picking, 2D classification, and 3D reconstruction. Existing cryo-EM denoising methods are commonly trained with pixel-wise or Noise2Noise-style objectives, which can improve visual quality but do not explicitly account for structural consistency required by downstream analysis. In this work, we propose a score-based denoising framework for cryo-EM that learns the clean-data score to recover particle signals while better preserving structural information. Building on this formulation, we further introduce a target-guided variant…
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