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...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10935353/ |
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| author | K. Deepthi Aditya K. Shastry |
| author_facet | K. Deepthi Aditya K. Shastry |
| author_sort | K. Deepthi |
| collection | DOAJ |
| description | 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 including technical hardware limitations, high cost, operator skills, data compatibility, and maintenance issues. This paper proposes Retinal-ESRGAN, a novel hybrid unsupervised GAN model particularly designed for retinal image super-resolution. The model incorporates architectural modifications with respect to generator and discriminator network using Google Colaboratory and TensorFlow 2.0 facilitating limited resource usage. To address resource constraints, a training strategy involving pausing and resuming in batches is implemented. The experiments conducted have demonstrated Retinal-ESRGAN’s potential that achieved an average PSNR of 35.22 dB and SSIM of 0.916, outperforming both SRGAN and ESRGAN showing PSNR metric improvement of 4.8% over SRGAN and 10.5% improvement over ESRGAN. Also, a 5.7% improvement over SRGAN and 22.4% improvement over ESRGAN in SSIM metric, Inception score of 6.02, Fréchet Inception Distance of 25.31 and accuracy of 94.98% utilizing significantly less training time and computational resources. |
| format | Article |
| id | doaj-art-34a200b4edb942998b8ec306ef8b0d5d |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-34a200b4edb942998b8ec306ef8b0d5d2025-08-20T02:53:41ZengIEEEIEEE Access2169-35362025-01-0113533665337610.1109/ACCESS.2025.355345710935353Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic AccuracyK. Deepthi0https://orcid.org/0000-0001-9626-7006Aditya K. Shastry1https://orcid.org/0000-0003-3920-576XDepartment of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Visvesvaraya Technological University, Bengaluru, IndiaDepartment of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Visvesvaraya Technological University, Bengaluru, IndiaMedical 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 including technical hardware limitations, high cost, operator skills, data compatibility, and maintenance issues. This paper proposes Retinal-ESRGAN, a novel hybrid unsupervised GAN model particularly designed for retinal image super-resolution. The model incorporates architectural modifications with respect to generator and discriminator network using Google Colaboratory and TensorFlow 2.0 facilitating limited resource usage. To address resource constraints, a training strategy involving pausing and resuming in batches is implemented. The experiments conducted have demonstrated Retinal-ESRGAN’s potential that achieved an average PSNR of 35.22 dB and SSIM of 0.916, outperforming both SRGAN and ESRGAN showing PSNR metric improvement of 4.8% over SRGAN and 10.5% improvement over ESRGAN. Also, a 5.7% improvement over SRGAN and 22.4% improvement over ESRGAN in SSIM metric, Inception score of 6.02, Fréchet Inception Distance of 25.31 and accuracy of 94.98% utilizing significantly less training time and computational resources.https://ieeexplore.ieee.org/document/10935353/ESRGANFréchet inception distancehigh resolution (HR) imageinception scorelow resolution (LR) imagePSNR |
| spellingShingle | K. Deepthi Aditya K. Shastry Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy IEEE Access ESRGAN Fréchet inception distance high resolution (HR) image inception score low resolution (LR) image PSNR |
| title | Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy |
| title_full | Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy |
| title_fullStr | Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy |
| title_full_unstemmed | Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy |
| title_short | Retinal-ESRGAN: A Hybrid GAN Model Approach for Retinal Image Super-Resolution Coupled With Reduced Training Time and Computational Resources for Improved Diagnostic Accuracy |
| title_sort | retinal esrgan a hybrid gan model approach for retinal image super resolution coupled with reduced training time and computational resources for improved diagnostic accuracy |
| topic | ESRGAN Fréchet inception distance high resolution (HR) image inception score low resolution (LR) image PSNR |
| url | https://ieeexplore.ieee.org/document/10935353/ |
| work_keys_str_mv | AT kdeepthi retinalesrganahybridganmodelapproachforretinalimagesuperresolutioncoupledwithreducedtrainingtimeandcomputationalresourcesforimproveddiagnosticaccuracy AT adityakshastry retinalesrganahybridganmodelapproachforretinalimagesuperresolutioncoupledwithreducedtrainingtimeandcomputationalresourcesforimproveddiagnosticaccuracy |