mlsanityData to 14 Oct 2026

MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections

arXiv 2403.10815 ↗First posted 16 Mar 2024eess.IV cs.CVcited by 6

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Volumetric optical microscopy using non-diffracting beams enables rapid imaging of 3D volumes by projecting them axially to 2D images but lacks crucial depth information. Addressing this, we introduce MicroDiffusion, a pioneering tool facilitating high-quality, depth-resolved 3D volume reconstruction from limited 2D projections. While existing Implicit Neural Representation (INR) models often yield incomplete outputs and Denoising Diffusion Probabilistic Models (DDPM) excel at capturing details, our method integrates INR's structural coherence with DDPM's fine-detail enhancement capabilities. We pretrain an INR model to transform 2D axially-projected images into a preliminary 3D volume. This pretrained INR acts as a global prior guiding DDPM's generative process through a linear interpolation between INR outputs and noise inputs. This strategy enriches the diffusion process with structured 3D information, enhancing detail and reducing noise in localized 2D images. By conditioning the diffusion model on the closest 2D projection, MicroDiffusion substantially enhances fidelity in resulting 3D reconstructions, surpassing INR and standard DDPM outputs with unparalleled image quality and structural fidelity. Our code and dataset are available at https://github.com/UCSC-VLAA/MicroDiffusion.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 28 of its 49 references that are in the map. First posted on arXiv 16 Mar 2024.

Builds on

28 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Implicit Diffusion Models for Continuous Super-Resolution29 Mar

Other references · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  6. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  7. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
  8. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  9. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  10. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
  11. 2022Magic3D: High-Resolution Text-to-3D Content Creationcited by 1,290
  12. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
15 more
  1. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
  2. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 560
  3. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  4. 2023Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priorscited by 397
  5. 2022SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generationcited by 288
  6. 20223D Neural Field Generation using Triplane Diffusioncited by 274
  7. 2022Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Modelscited by 151
  8. 2021NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstructioncited by 146
  9. 2023Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Datacited by 113
  10. 2023HoloDiffusion: Training a 3D Diffusion Model using 2D Imagescited by 106
  11. 2019Reconstructing continuous distributions of 3D protein structure from cryo-EM imagescited by 56
  12. 2021An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representationcited by 48
  13. 2023Polynomial Implicit Neural Representations For Large Diverse Datasetscited by 18
  14. 2021SiReN: Sign-Aware Recommendation Using Graph Neural Networkscited by 9
  15. 2023FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representationscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024Where Do We Stand with Implicit Neural Representations? A Technical and Performance Surveycited by 42
  2. 2025GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstructioncited by 4
  3. 2025V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopycited by 2
  4. 2025I-INR: Iterative Implicit Neural Representationscited by 2
  5. 2024From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Taskcited by 0
  6. 2026GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstructioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopy
  2. 2024Reference-free Axial Super-resolution of 3D Microscopy Images using Implicit Neural Representation with a 2D Diffusion Prior
  3. 2023DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images
  4. 2024Denoising diffusion models for high-resolution microscopy image restoration
  5. 2022RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
  6. 2025Light Transport-aware Diffusion Posterior Sampling for Single-View Reconstruction of 3D Volumes
  7. 2025EMGauss: Continuous Slice-to-3D Reconstruction via Dynamic Gaussian Modeling in Volume Electron Microscopy
  8. 2024Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial