# Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement

arXiv: https://arxiv.org/abs/2303.04603 · HTML: https://mlsanity.com/paper/2303.04603/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [Towards performant and reliable undersampled MR reconstruction via diffusion model sampling](https://mlsanity.com/paper/2203.04292/) (2022)
- [Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces](https://mlsanity.com/paper/1907.05345/) (2019)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges](https://mlsanity.com/paper/2202.06511/) (2022)
- [Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images](https://mlsanity.com/paper/2107.08274/) (2021)
- [An Annotation-free Restoration Network for Cataractous Fundus Images](https://mlsanity.com/paper/2203.07737/) (2022)
- [Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network](https://mlsanity.com/paper/2210.09606/) (2022)
- [Structure-consistent Restoration Network for Cataract Fundus Image Enhancement](https://mlsanity.com/paper/2206.04684/) (2022)
- [Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis](https://mlsanity.com/paper/1805.06625/) (2018)
- [JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection](https://mlsanity.com/paper/2203.00461/) (2022)

## Built on by (most cited first, 2 of 2)

- [Fundus image enhancement through direct diffusion bridges](https://mlsanity.com/paper/2409.12377/) (2024)
- [Joint Medical Image Enhancement and Segmentation with Diffusion-based Symbiotic Information Interaction](https://mlsanity.com/paper/2607.00058/) (2026)

## Abstract (arXiv)

The quality of a fundus image can be compromised by numerous factors, many of which are challenging to be appropriately and mathematically modeled. In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images. Specifically, we first adopt a data-driven degradation framework to learn degradation mappings from unpaired high-quality to low-quality images. We then apply a conditional diffusion model to learn the inverse enhancement process in a paired manner. The proposed LED is able to output enhancement results that maintain clinically important features with better clarity. Moreover, in the inference phase, LED can be easily and effectively integrated with any existing fundus image enhancement framework. We evaluate the proposed LED on several downstream tasks with respect to various clinically-relevant metrics, successfully demonstrating its superiority over existing state-of-the-art methods both quantitatively and qualitatively. The source code is available at https://github.com/QtacierP/LED.