mlsanityData to 7 Oct 2026

Denoising diffusion models for high-resolution microscopy image restoration

arXiv 2409.12078 ↗First posted 18 Sep 2024eess.IV cs.CV cs.LGcited by 0

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

Abstract (arXiv)

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluorophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 32 of its 65 references that are in the map. First posted on arXiv 18 Sep 2024.

Builds on

32 of 65 references.

References · 32

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  7. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  8. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  9. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  10. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  11. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  12. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
20 more
  1. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  2. 2021Label-Efficient Semantic Segmentation with Diffusion Modelscited by 495
  3. 2018Noise2Void - Learning Denoising from Single Noisy Imagescited by 359
  4. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Modelscited by 279
  5. 2022Diffusion Models for Medical Anomaly Detectioncited by 231
  6. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  7. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Modelscited by 191
  8. 2022Equivariant 3D-Conditional Diffusion Models for Molecular Linker Designcited by 121
  9. 2018A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Imagescited by 70
  10. 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
  11. 2022PET image denoising based on denoising diffusion probabilistic modelscited by 57
  12. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 51
  13. 2023Stochastic Segmentation with Conditional Categorical Diffusion Modelscited by 38
  14. 2021Noise2Fast: Fast Self-Supervised Single Image Blind Denoisingcited by 28
  15. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoisingcited by 21
  16. 2022Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Modelcited by 16
  17. 2023Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Actioncited by 15
  18. 2023Deep Ultrasound Denoising Using Diffusion Probabilistic Modelscited by 12
  19. 2022Latent Space Diffusion Models of Cryo-EM Structurescited by 6
  20. 2023Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANscited by 4

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  1. 2024Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
  2. 2023Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
  3. 2024Diffusion Posterior Proximal Sampling for Image Restoration
  4. 2023The Missing U for Efficient Diffusion Models
  5. 2024Multi-scale Conditional Generative Modeling for Microscopic Image Restoration
  6. 2022Complex-valued Retrievals From Noisy Images Using Diffusion Models
  7. 2022Denoising Diffusion Restoration Models
  8. 2023PartDiff: Image Super-resolution with Partial Diffusion Models