ArticleScientific reports2024
Retinal fundus image super-resolution based on generative adversarial network guided with vascular structure prior.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Adaptive MDA-Net: Degradation-Aware Retinal Fundus Image Enhancement with Frequency-Domain and Illumination Guidance for Clinical Screening.Diagnostics (Basel, Switzerland) · 2026Article
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- Towards Reliable Healthcare Imaging: A Multifaceted Approach in Class Imbalance Handling for Medical Image Segmentation.Interdisciplinary sciences, computational life sciences · 2025Article
- A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging.Physical and engineering sciences in medicine · 2025Article
- Dual branch attention network for image super-resolution.Scientific reports · 2025Article
- Cytokines in age-related eye diseases: pathogenesis and potential targets for innovative therapies.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Ensemble-based eye disease detection system utilizing fundus and vascular structures.Scientific reports · 2025Article
- An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution.Scientific reports · 2025Article
- Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification.Scientific reports · 2025Article
- MDEU-Net: Medical Image Segmentation Network Based on Multi-Head Multi-Scale Cross-Axis.Sensors (Basel, Switzerland) · 2025Article
- Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture.Scientific reports · 2025Article
- Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images.Scientific reports · 2025Article
- Research on recognition of diabetic retinopathy hemorrhage lesions based on fine tuning of segment anything model.Scientific reports · 2025Article
- Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.BMC infectious diseases · 2025Article
- Video super resolution based on deformable 3D convolutional group fusion.Scientific reports · 2025Article
- Ensemble fuzzy deep learning for brain tumor detection.Scientific reports · 2025Article
- An automatic cervical cell classification model based on improved DenseNet121.Scientific reports · 2025Article
- Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Many ophthalmic and systemic diseases can be screened by analyzing retinal fundus images. The clarity and resolution of retinal fundus images directly determine the effectiveness of clinical diagnosis. Deep learning methods based on generative adversarial networks are used in various research fields due to their powerful generative capabilities, especially image super-resolution. Although Real-ESRGAN is a recently proposed method that excels in processing real-world degraded images, it suffers from structural distortions when super-resolving retinal fundus images are rich in structural information. To address this shortcoming, we first process the input image using a pre-trained U-Net model to obtain a structural segmentation map of the retinal vessels and use the segmentation map as the structural prior. The spatial feature transform layer is then used to better integrate the structural prior into the generation process of the generator. In addition, we introduce channel and spatial attention modules into the skip connections of the discriminator to emphasize meaningful features and accordingly enhance the discriminative power of the discriminator. Based on the original loss functions, we introduce the L1 loss function to measure the pixel-level differences between the segmentation maps of retinal vascular structures in the high-resolution images and the super-resolution images to further constrain the super-resolution images. Simulation results on retinal image datasets show that our improved algorithm results have a better visual performance by suppressing structural distortions in the super-resolution images.
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