Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy
Medical Image Super-Resolution has always been a subject of interest in medical image processing. However, super-resolved retinal images are a requisite tool for doctors to properly diagnose and treat ophthalmic diseases. The acquisition of high-quality images is challenging owing to several factors...
Saved in:
| Main Authors: | K. Deepthi, Aditya K. Shastry |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10935353/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Super-Resolution Reconstruction of Coarse Aggregate Particle Images for Earth–Rock Dam Construction Based on Real-ESRGAN
by: Shuangping Li, et al.
Published: (2025-06-01) -
Super-Resolution of Medical Images Using Real ESRGAN
by: Priyanka Nandal, et al.
Published: (2024-01-01) -
Optimization of Imaging Reconnaissance Systems Using Super-Resolution: Efficiency Analysis in Interference Conditions
by: Marta Bistroń, et al.
Published: (2024-12-01) -
Task-aware conditional GAN with multi-objective loss for realistic and efficient industrial time series generation
by: Kai Lang, et al.
Published: (2025-08-01) -
An Ensemble Learning Approach for Glaucoma Detection in Retinal Images
by: Marwah M. Mahdi, et al.
Published: (2022-12-01)