# Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis

arXiv: https://arxiv.org/abs/1805.06625 · HTML: https://mlsanity.com/paper/1805.06625/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 12 of 12)

- [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)
- [A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning](https://mlsanity.com/paper/2309.00885/) (2023)
- [Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2409.07862/) (2024)
- [Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement](https://mlsanity.com/paper/2312.01338/) (2023)
- [Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework](https://mlsanity.com/paper/2302.11795/) (2023)
- [Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement](https://mlsanity.com/paper/2303.04603/) (2023)
- [Metropolis Theorem and Its Applications in Single Image Detail Enhancement](https://mlsanity.com/paper/2302.09762/) (2023)
- [Fundus image enhancement through direct diffusion bridges](https://mlsanity.com/paper/2409.12377/) (2024)
- [Versatile Cataract Fundus Image Restoration Model Utilizing Unpaired Cataract and High-quality Images](https://mlsanity.com/paper/2411.12278/) (2024)
- [Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement](https://mlsanity.com/paper/2505.01831/) (2025)

## Abstract (arXiv)

Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degeneration and diabetic retinopathy. With the development of computer science, computer aided diagnosis has been developed to process and analyse the retinal images automatically. One of the challenges in the analysis is that the quality of the retinal image is often degraded. For example, a cataract in human lens will attenuate the retinal image, just as a cloudy camera lens which reduces the quality of a photograph. It often obscures the details in the retinal images and posts challenges in retinal image processing and analysing tasks. In this paper, we approximate the degradation of the retinal images as a combination of human-lens attenuation and scattering. A novel structure-preserving guided retinal image filtering (SGRIF) is then proposed to restore images based on the attenuation and scattering model. The proposed SGRIF consists of a step of global structure transferring and a step of global edge-preserving smoothing. Our results show that the proposed SGRIF method is able to improve the contrast of retinal images, measured by histogram flatness measure, histogram spread and variability of local luminosity. In addition, we further explored the benefits of SGRIF for subsequent retinal image processing and analysing tasks. In the two applications of deep learning based optic cup segmentation and sparse learning based cup-to-disc ratio (CDR) computation, our results show that we are able to achieve more accurate optic cup segmentation and CDR measurements from images processed by SGRIF.