ArticleAdvances in ophthalmology practice and research
A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera.
Article in Advances in ophthalmology practice and research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 14 citations in OpenAlex.
- Adaptive MDA-Net: Degradation-Aware Retinal Fundus Image Enhancement with Frequency-Domain and Illumination Guidance for Clinical Screening.Diagnostics (Basel, Switzerland) · 2026Article
- Retinal image-based disease classification using hybrid deep architecture with improved image features.International ophthalmology · 2025Article
- The retinal age gap: an affordable and highly accessible biomarker for population-wide disease screening across the globe.Proceedings. Biological sciences · 2025Article
- A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials.Biomedicines · 2025Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Due to limited imaging conditions, the quality of fundus images is often unsatisfactory, especially for images photographed by handheld fundus cameras. Here, we have developed an automated method based on combining two mirror-symmetric generative adversarial networks (GANs) for image enhancement. Methods: A total of 1047 retinal images were included. The raw images were enhanced by a GAN-based deep enhancer and another methods based on luminosity and contrast adjustment. All raw images and enhanced images were anonymously assessed and classified into 6 levels of quality classification by three experienced ophthalmologists. The quality classification and quality change of images were compared. In addition, image-detailed reading results for the number of dubiously pathological fundi were also compared. Results: After GAN enhancement, 42.9% of images increased their quality, 37.5% remained stable, and 19.6% decreased. After excluding the images at the highest level (level 0) before enhancement, a large number (75.6%) of images showed an increase in quality classification, and only a minority (9.3%) showed a decrease. The GAN-enhanced method was superior for quality improvement over a luminosity and contrast adjustment method ( Conclusions: Learning the style of high-quality retinal images based on the proposed deep enhancer may be an effective way to improve the quality of retinal images photographed by handheld fundus cameras.
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