RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images
This addresses the issue of diagnostic uncertainty in eye disease detection for clinicians, but it appears incremental as it combines existing methods like Diffusion Models and VAEs.
The study tackled the problem of blurred or unreadable fundus images by proposing RetinaRegen, a hybrid model for retinal image restoration, achieving a PSNR of 27.4521, an SSIM of 0.9556, and an LPIPS of 0.1911 for the optic disc region.
Fundus image quality is crucial for diagnosing eye diseases, but real-world conditions often result in blurred or unreadable images, increasing diagnostic uncertainty. To address these challenges, this study proposes RetinaRegen, a hybrid model for retinal image restoration that integrates a readability classifi-cation model, a Diffusion Model, and a Variational Autoencoder (VAE). Ex-periments on the SynFundus-1M dataset show that the proposed method achieves a PSNR of 27.4521, an SSIM of 0.9556, and an LPIPS of 0.1911 for the readability labels of the optic disc (RO) region. These results demonstrate superior performance in restoring key regions, offering an effective solution to enhance fundus image quality and support clinical diagnosis.