Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images

TL;DR

This paper introduces an unsupervised diffusion autoencoder that improves artifact removal in handheld fundus images, enhancing diagnostic accuracy without needing paired training data.

Contribution

It presents a novel unsupervised model combining a diffusion autoencoder with a context encoder for effective artifact restoration in unstructured, real-world fundus images.

Findings

Diagnostic accuracy increased to 81.17% on unseen data.

Model effectively restores images affected by various artifacts.

Unsupervised training on high-quality images suffices for robust restoration.

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. To address this, we propose an unsupervised diffusion autoencoder that integrates a context encoder with the denoising process to learn semantically meaningful representations for artifact restoration. The model is trained only on high-quality table-top fundus images and infers to restore artifact-affected handheld acquisitions. We validate the restorations through quantitative and qualitative evaluations, and have shown that diagnostic accuracy increases to 81.17% on an unseen dataset and multiple artifact conditions