J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume
Liu, Xiwei Kassab, Mohamad Xu, Min Ho, Qirong
Liu, Xiwei
Kassab, Mohamad
Xu, Min
Ho, Qirong
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Cryo-Electron Tomography (Cryo-ET) enables detailed 3D visualization of cellular structures in near-native states but suffers from low signal-to-noise ratio due to imaging constraints. Traditional denoising methods and supervised learning approaches often struggle with complex noise patterns and the lack of paired datasets. Self-supervised methods, which utilize noisy input itself as a target, have been studied; however, existing Cryo-ET self-supervised denoising methods face significant challenges due to losing information during training and the learned incomplete noise patterns. In this paper, we propose a novel self-supervised learning model that denoises Cryo-ET volumetric images using a single noisy volume. Our method features a U-shape J-invariant blind spot network with sparse centrally masked convolutions, dilated channel attention blocks, and volume-unshuffle/shuffle technique. The volume-unshuffle/shuffle technique expands receptive fields and utilizes multi-scale representations, significantly improving noise reduction and structural preservation. Experimental results demonstrate that our approach achieves superior performance compared to existing methods, advancing Cryo-ET data processing for structural biology research. Code is available at https://github.com/Xiwei-web/SelfCryoET.
Citation
X. Liu, M. Kassab, M. Xu, and Q. Ho, “J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume,” Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025, pp. 568–577, 2025, doi: 10.1109/WACV61041.2025.00065
Source
Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025
Conference
2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025
Keywords
Computer vision, Cryo-ET, Self-supervised, Denoising, 3D images processing
Subjects
Source
2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025
Publisher
IEEE