Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images
Summary: arXiv:2604.15723v1 Announce Type: cross
Abstract
The advent of handheld fundus imaging devices has made ophthalmologic diagnosis and disease screening more accessible, efficient, and cost-effective. However, images captured from these setups often suffer from artifacts such as flash reflections, exposure variations, and motion-induced blur, which degrade image quality and hinder downstream analysis. While generative models have been effective in image restoration, most depend on paired supervision or predefined artifact structures, making them less adaptable to unstructured degradations commonly observed in handheld fundus images.
Introduction
In recent years, the deployment of handheld fundus cameras has revolutionized the field of ophthalmology, facilitating widespread screening and diagnosis of retinal diseases. These devices offer significant advantages in terms of portability and cost, making them invaluable in various healthcare settings. However, the quality of images produced by handheld devices is often compromised by several artifacts that can obscure critical diagnostic information.
Challenges in Image Restoration
The primary challenges associated with restoring handheld fundus images stem from:
- Flash Reflections: Glare from the camera flash can obscure vital details in the retinal images.
- Exposure Variations: Inconsistent lighting conditions can lead to images that are either too dark or too bright.
- Motion-Induced Blur: Handheld imaging often results in motion artifacts that blur the captured images, leading to loss of detail.
Proposed Solution: Unsupervised Diffusion Autoencoder
To address these challenges, we propose an unsupervised diffusion autoencoder. This innovative model combines a context encoder with the denoising process, enabling it to learn semantically meaningful representations specifically for artifact restoration. By training solely on high-quality table-top fundus images, our model can effectively infer and restore handheld acquisitions afflicted by various artifacts.
Methodology
The unsupervised approach allows for flexibility in dealing with the unstructured degradations that are typical in handheld fundus images. The diffusion autoencoder operates by:
- Learning from High-Quality Images: The model is trained on clean, high-quality fundus images, allowing it to understand the underlying features of healthy retinal images.
- Applying Denoising Techniques: The integrated denoising process helps in mitigating the identified artifacts effectively.
- Contextual Encoding: By understanding the context of the images, the autoencoder can more accurately restore images that exhibit various types of degradation.
Results and Validation
We conducted extensive quantitative and qualitative evaluations to validate the effectiveness of the proposed model. The results indicate a significant improvement in diagnostic accuracy, achieving a score of 81.17% on an unseen dataset across multiple artifact conditions. This demonstrates the model’s robustness and its potential to enhance clinical decision-making.
Conclusion
The development of an unsupervised diffusion autoencoder presents a promising advancement in the field of ophthalmic imaging. By effectively restoring handheld fundus images, this model not only enhances image quality but also supports improved diagnostic accuracy, ultimately contributing to better patient outcomes in ophthalmology.