NoiseFlow : Learning Optical Flow from Low SNR Cryo-EM Movie

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition (ICPR)
PublisherIEEE
Pages3471-3477
ISBN (Electronic)978-1-6654-9062-7
ISBN (Print)978-1-6654-9063-4
Publication statusPublished - 2022

Publication series

Name
ISSN (Print)1051-4651
ISSN (Electronic)2831-7475

Conference

Title26th International Conference on Pattern Recognition (ICPF 2022)
LocationPalais des congrès de Montréal
PlaceCanada
CityMontréal
Period21 - 25 August 2022

Abstract

Cryo-EM movie in single particle analysis has extremely low SNR and requires aligning multiple frames to achieve signal enhancement. Currently, signal processing technique is adopted to estimate the motion vector between a pair of cryo-EM movie frames at patch-level, and the estimated motion vector is used as the reference for frame alignment, whose accuracy will determine the resolution of the reconstructed 3D structure of the particle. The patch-level motion may not well represent the beam-induced motion of particles since particles in a patch move towards different directions due to beam striking. However, the low SNR of cryo-EM movie makes it difficult to estimate the motion of particles at pixel-level. Meanwhile, existing optical flow estimation models only consider the ideal case where high-quality videos are provided, which fail to obtain optical flow from cryo-EM movie. In this paper, we diminish this limitation by proposing a model called NoiseFlow, a deep learning network for optical flow estimation from low SNR cryo-EM movie. NoiseFlow makes use of the multi-frame stacking module and the denoising module to extract noise-invariant features, and then computes the correlation volume from noise-invariant features to learn optical flow. For evaluation, we train our model on two synthetic cryo-EM movie datasets and infer on real cryo-EM data. The experimental results illustrate that NoiseFlow achieves state-of-the-art performance on both synthetic and real cryo-EM datasets.

Citation Format(s)

NoiseFlow : Learning Optical Flow from Low SNR Cryo-EM Movie. / Chong, Xiaoya; Zhou, Niyun; Li, Qing et al.

2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 2022. p. 3471-3477.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review