mlsanityData to 7 Oct 2026

Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model

arXiv 2201.11760 ↗First posted 27 Jan 2022cs.CVcited by 16

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-free reference image for supervision. In this study, we present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. A diffusion process is defined by adding a sequence of Gaussian noise to self-fused OCT b-scans. Then the reverse process of diffusion, modeled by a Markov chain, provides an adjustable level of denoising. Our experiment results demonstrate that our method can significantly improve the image quality with a simple working pipeline and a small amount of training data.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 7 of its 17 references that are in the map. First posted on arXiv 27 Jan 2022.

Builds on

7 of 17 references.

References · 7

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2017Deep Image Priorcited by 1,097
  4. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  5. 2018A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Headcited by 11
  6. 2021LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentationcited by 9
  7. 2021Retinal OCT Denoising with Pseudo-Multimodal Fusion Networkcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 6; 2024: 3; 2025: 3.

16 papers.

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  1. 2022Diffusion Models in Vision: A Surveycited by 1,008
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  3. 2022A Survey on Generative Diffusion Modelcited by 238
  4. 2022GENIE: Higher-Order Denoising Diffusion Solverscited by 127
  5. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Imagescited by 45
  6. 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
  7. 2023Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancementcited by 8
  8. 2023Content-Preserving Diffusion Model for Unsupervised AS-OCT image Despecklingcited by 5
  9. 2023A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Modelscited by 5
  10. 2023Unsupervised Discovery of 3D Hierarchical Structure with Generative Diffusion Featurescited by 4
  11. 2023Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentationcited by 4
  12. 2024FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbationcited by 4
4 more
  1. 2025Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRIcited by 3
  2. 2025Causal Disentanglement for Robust Long-tail Medical Image Generationcited by 2
  3. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Reviewcited by 1
  4. 2024Denoising diffusion models for high-resolution microscopy image restorationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025GARD: Gamma-based Anatomical Restoration and Denoising for Retinal OCT
  2. 2026Super-Resolution and Denoising of Corneal B-Scan OCT Imaging Using Diffusion Model Plug-and-Play Priors
  3. 2025Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors
  4. 2024Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models
  5. 2024Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution
  6. 2025Deep Generative Models for Enhanced Vitreous OCT Imaging
  7. 2024Denoising OCT Images Using Steered Mixture of Experts with Multi-Model Inference
  8. 2026DualDiT: A Conditional Dual-Output Diffusion Transformer for Joint OCT Image and Segmentation Mask Generation