RetinaRegen融合多种模型，修复模糊眼底图像关键区域。
RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images
- 结合分类器、扩散模型与变分自编码器，分步提升图像可读性。
- 在视盘区域实现27.45的PSNR、0.9556的SSIM和0.1911的LPIPS。
- 适合眼科影像增强与临床辅助诊断场景使用。
眼底图像质量对眼病诊断至关重要，但现实条件常导致图像模糊或不可读，增加诊断不确定性。为此，本文提出RetinaRegen，一种融合可读性分类模型、扩散模型与变分自编码器（VAE）的混合眼底图像修复方法。在SynFundus-1M数据集上的实验表明，该方法在视盘（RO）区域的可读性标签重建中达到27.4521的PSNR、0.9556的SSIM和0.1911的LPIPS，显著优于现有方法，有效提升了关键区域的恢复质量，为改善眼底图像清晰度与支持临床诊断提供了可靠方案。
原文摘要 · Abstract (English)
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.
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