mlsanityData to 7 Oct 2026

Z-upscaling: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM Volumes

arXiv 2410.07043 ↗First posted 9 Oct 2024eess.IV cs.CVcited by 2

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Abstract (arXiv)

We propose a novel optical flow based approach to enhance the axial resolution of anisotropic 3D EM volumes to achieve isotropic 3D reconstruction. Assuming spatial continuity of 3D biological structures in well aligned EM volumes, we reasoned that optical flow estimation techniques, often applied for temporal resolution enhancement in videos, can be utilized. Pixel level motion is estimated between neighboring 2D slices along z, using spatial gradient flow estimates to interpolate and generate new 2D slices resulting in isotropic voxels. We leverage recent state-of-the-art learning methods for video frame interpolation and transfer learning techniques, and demonstrate the success of our approach on publicly available ultrastructure EM volumes.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 9 Oct 2024.

Builds on

9 of 15 references.

References · 9

  1. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  2. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  3. 2017Video Enhancement with Task-Oriented Flowcited by 612
  4. 2019Depth-Aware Video Frame Interpolationcited by 232
  5. 2022FILM: Frame Interpolation for Large Motioncited by 149
  6. 2020FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolationcited by 97
  7. 2017Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopycited by 12
  8. 2023Reference-Free Isotropic 3D EM Reconstruction using Diffusion Modelscited by 6
  9. 2023SSTM: Spatiotemporal Recurrent Transformers for Multi-frame Optical Flow Estimationcited by 2

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.