# Content-Preserving Diffusion Model for Unsupervised AS-OCT image Despeckling

arXiv: https://arxiv.org/abs/2306.17717 · HTML: https://mlsanity.com/paper/2306.17717/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 11 of 11)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction](https://mlsanity.com/paper/2112.05146/) (2021)
- [MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion](https://mlsanity.com/paper/2203.12621/) (2022)
- [Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks](https://mlsanity.com/paper/2007.02075/) (2020)
- [Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model](https://mlsanity.com/paper/2201.11760/) (2022)
- [Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data](https://mlsanity.com/paper/2208.00402/) (2022)
- [A Newton Solver for Micromorphic Computational Homogenization Enabling Multiscale Buckling Analysis of Pattern-Transforming Metamaterials](https://mlsanity.com/paper/2008.12850/) (2020)

## Built on by (most cited first, 5 of 5)

- [Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges](https://mlsanity.com/paper/2407.00116/) (2024)
- [CPDM: Content-Preserving Diffusion Model for Underwater Image Enhancement](https://mlsanity.com/paper/2401.15649/) (2024)
- [GARD: Gamma-based Anatomical Restoration and Denoising for Retinal OCT](https://mlsanity.com/paper/2509.10341/) (2025)
- [Deep Generative Models for Enhanced Vitreous OCT Imaging](https://mlsanity.com/paper/2511.00881/) (2025)
- [Test-Time Adaptation in Optical Coherence Tomography Using Trajectory-Aligned Time-Independent Flow](https://mlsanity.com/paper/2606.18876/) (2026)

## Abstract (arXiv)

Anterior segment optical coherence tomography (AS-OCT) is a non-invasive imaging technique that is highly valuable for ophthalmic diagnosis. However, speckles in AS-OCT images can often degrade the image quality and affect clinical analysis. As a result, removing speckles in AS-OCT images can greatly benefit automatic ophthalmology analysis. Unfortunately, challenges still exist in deploying effective AS-OCT image denoising algorithms, including collecting sufficient paired training data and the requirement to preserve consistent content in medical images. To address these practical issues, we propose an unsupervised AS-OCT despeckling algorithm via Content Preserving Diffusion Model (CPDM) with statistical knowledge. At the training stage, a Markov chain transforms clean images to white Gaussian noise by repeatedly adding random noise and removes the predicted noise in a reverse procedure. At the inference stage, we first analyze the statistical distribution of speckles and convert it into a Gaussian distribution, aiming to match the fast truncated reverse diffusion process. We then explore the posterior distribution of observed images as a fidelity term to ensure content consistency in the iterative procedure. Our experimental results show that CPDM significantly improves image quality compared to competitive methods. Furthermore, we validate the benefits of CPDM for subsequent clinical analysis, including ciliary muscle (CM) segmentation and scleral spur (SS) localization.