# Progressive Transfer Learning for Multi-Pass Fundus Image Restoration

arXiv: https://arxiv.org/abs/2504.10025 · HTML: https://mlsanity.com/paper/2504.10025/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading](https://mlsanity.com/paper/1911.01376/) (2019)
- [Blind ECG Restoration by Operational Cycle-GANs](https://mlsanity.com/paper/2202.00589/) (2022)
- [Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading](https://mlsanity.com/paper/1808.10564/) (2018)
- [The Multiscale Bowler-Hat Transform for Blood Vessel Enhancement in Retinal Images](https://mlsanity.com/paper/1709.05495/) (2017)

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

- [Expert Operational GANS: Towards Real-Color Underwater Image Restoration](https://mlsanity.com/paper/2507.11562/) (2025)
- [Multi-Pass, Multi-View Blended Learning for High-Fidelity Volumetric CT Synthesis from Chest X-Rays](https://mlsanity.com/paper/2609.09920/) (2026)

## Abstract (arXiv)

Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of poor quality fundus images caused by factors such as inadequate illumination, noise, blurring and other motion artifacts yields a significant challenge for accurate DR screening. In this study, we propose progressive transfer learning for multi pass restoration to iteratively enhance the quality of degraded fundus images, ensuring more reliable DR screening. Unlike previous methods that often focus on a single pass restoration, multi pass restoration via PTL can achieve a superior blind restoration performance that can even improve most of the good quality fundus images in the dataset. Initially, a Cycle GAN model is trained to restore low quality images, followed by PTL induced restoration passes over the latest restored outputs to improve overall quality in each pass. The proposed method can learn blind restoration without requiring any paired data while surpassing its limitations by leveraging progressive learning and fine tuning strategies to minimize distortions and preserve critical retinal features. To evaluate PTL's effectiveness on multi pass restoration, we conducted experiments on DeepDRiD, a large scale fundus imaging dataset specifically curated for diabetic retinopathy detection. Our result demonstrates state of the art performance, showcasing PTL's potential as a superior approach to iterative image quality restoration.