# Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network

arXiv: https://arxiv.org/abs/2210.09606 · HTML: https://mlsanity.com/paper/2210.09606/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 18 Oct 2022.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015)
- [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces](https://mlsanity.com/paper/1907.05345/) (2019)
- [An Annotation-free Restoration Network for Cataractous Fundus Images](https://mlsanity.com/paper/2203.07737/) (2022)
- [Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis](https://mlsanity.com/paper/1805.06625/) (2018)
- [Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey](https://mlsanity.com/paper/2012.04830/) (2020)

## Built on by (most cited first, 18 of 18)

- [A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement](https://mlsanity.com/paper/2301.08880/) (2023)
- [CUNSB-RFIE: Context-aware Unpaired Neural Schr"odinger Bridge in Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2409.10966/) (2024)
- [Image Quality-aware Diagnosis via Meta-knowledge Co-embedding](https://mlsanity.com/paper/2303.15038/) (2023)
- [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)
- [CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching](https://mlsanity.com/paper/2511.21024/) (2025)
- [Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement](https://mlsanity.com/paper/2303.04603/) (2023)
- [Fundus image enhancement through direct diffusion bridges](https://mlsanity.com/paper/2409.12377/) (2024)
- [Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement](https://mlsanity.com/paper/2505.01831/) (2025)
- [A Frequency-Aware Self-Supervised Learning for Ultra-Wide-Field Image Enhancement](https://mlsanity.com/paper/2508.19664/) (2025)
- [Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images](https://mlsanity.com/paper/2511.13353/) (2025)
- [VAOT: Vessel-Aware Optimal Transport for Retinal Fundus Enhancement](https://mlsanity.com/paper/2511.18763/) (2025)
- [SGW-GAN: Sliced Gromov-Wasserstein Guided GANs for Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2601.13417/) (2026)
- [Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement](https://mlsanity.com/paper/2604.03806/) (2026)
- [Joint Medical Image Enhancement and Segmentation with Diffusion-based Symbiotic Information Interaction](https://mlsanity.com/paper/2607.00058/) (2026)
- [A Two-Stage Multi-Scale Attention-Based Network for Weakly Supervised Cataract Fundus Image Enhancement](https://mlsanity.com/paper/2609.20222/) (2026)

## Abstract (arXiv)

As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer from quality degradation caused by imaging interferences, leading to misdiagnosis. Despite impressive enhancement performances that state-of-the-art methods have achieved, challenges remain in clinical scenarios. For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data. Firstly, high-quality images are randomly degraded to form sequences of low-quality ones sharing the same content (SeqLCs). Then individual low-quality images are decomposed to Laplacian pyramid features (LPF) as the multi-level input for the enhancement. Subsequently, a feature pyramid constraint (FPC) for the sequence is introduced to enforce the PCE-Net to learn a degradation-invariant model. Extensive experiments have been conducted under the evaluation metrics of enhancement and segmentation. The effectiveness of the PCE-Net was demonstrated in comparison with state-of-the-art methods and the ablation study. The source code of this study is publicly available at https://github.com/HeverLaw/PCENet-Image-Enhancement.