Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement

• URL: http://arxiv.org/abs/2604.03806v1
• Date: Sat, 04 Apr 2026 17:24:23 GMT
• Title: Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement
• Abstract summary: EyeBench-V2 is a benchmark designed to bridge the gap between enhancement model performance and clinical utility.<n>Our benchmark provides a rigorous, task-oriented analysis of existing generative models.
• Score: 13.241000354696903
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Over the past decade, generative models have demonstrated success in enhancing fundus images. However, the evaluation of these models remains a challenge. A benchmark for fundus image enhancement is needed for three main reasons:(1) Conventional denoising metrics such as PSNR and SSIM fail to capture clinically relevant features, such as lesion preservation and vessel morphology consistency, limiting their applicability in real-world settings; (2) There is a lack of unified evaluation protocols that address both paired and unpaired enhancement methods, particularly those guided by clinical expertise; and (3) An evaluation framework should provide actionable insights to guide future advancements in clinically aligned enhancement models. To address these gaps, we introduce EyeBench-V2, a benchmark designed to bridge the gap between enhancement model performance and clinical utility. Our work offers three key contributions:(1) Multi-dimensional clinical-alignment through downstream evaluations: Beyond standard enhancement metrics, we assess performance across clinically meaningful tasks including vessel segmentation, diabetic retinopathy (DR) grading, generalization to unseen noise patterns, and lesion segmentation. (2) Expert-guided evaluation design: We curate a novel dataset enabling fair comparisons between paired and unpaired enhancement methods, accompanied by a structured manual assessment protocol by medical experts, which evaluates clinically critical aspects such as lesion structure alterations, background color shifts, and the introduction of artificial structures. (3) Actionable insights: Our benchmark provides a rigorous, task-oriented analysis of existing generative models, equipping clinical researchers with the evidence needed to make informed decisions, while also identifying limitations in current methods to inform the design of next-generation enhancement models.

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