Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution

  • Zelin Li
  • , Chenwei Wang
  • , Zhaoke Huang*
  • , Yiming Ma
  • , Cunming Zhao
  • , Zhongying Zhao
  • , Hong Yan
  • *Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

3D fluorescence microscopy is essential for understanding fundamental life processes through long-term live-cell imaging. However, due to inherent issues in imaging principles, it faces significant challenges including spatially varying noise and anisotropic resolution, where the axial resolution lags behind the lateral resolution up to 4.5 times. Meanwhile, laser power is kept low to maintain cell viability, leading to inaccessible low-noise and high-resolution paired ground truth (GT). To tackle these limitations, a dual Cycle-consistent Diffusion is proposed to effectively mine intra-volume imaging priors within 3D cell volumes in an unsupervised manner, i.e., Volume Tells (VTCD), achieving de-noising and super-resolution (SR) simultaneously. Specifically, a spatially iso-distributed denoiser is designed to exploit the noise distribution consistency between adjacent low-noise and high-noise regions within the 3D cell volume, suppressing the spatially varying noise. Then, in light of the structural consistency of the cell volume, a cross-plane global-propagation SR module propagates high-resolution details from the XY plane into adjacent regions in the XZ and YZ planes, progressively enhancing resolution across the entire 3D cell volume. Experimental results on 10 in vivo cellular dataset demonstrate high improvements in both de-noising and super-resolution, with axial resolution enhanced from ~ 430 nm to ~ 90 nm.
Original languageEnglish
Title of host publication2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Editors Cristina Ceballos
Place of PublicationNashville
PublisherIEEE
Pages16091-16100
Number of pages10
ISBN (Electronic)9798331543648
ISBN (Print)9798331543655
DOIs
Publication statusPublished - 17 Jun 2025
EventThe IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025 - Music City Center, Nashville, United States
Duration: 11 Jun 202515 Jun 2025
https://cvpr.thecvf.com/Conferences/2025 (Conference website)
https://ieeexplore.ieee.org/xpl/conhome/11091818/proceeding (Conference proceedings)
https://media.eventhosts.cc/Conferences/CVPR2025/CVPR_main_conf_2025.pdf (Conference program)

Publication series

NameIEEE/CVF Conference on Computer Vision and Pattern Recognition
PublisherIEEE
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

ConferenceThe IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025
Abbreviated titleCVPR 2025
Country/TerritoryUnited States
CityNashville
Period11/06/2515/06/25
Internet address

User-Defined Keywords

  • super-resolution
  • de-noising
  • cell images
  • fluorescence microscopy
  • time-lapse 3d

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