Abstract
Recent image Super-Resolution (SR) models are achieving impressive effects in reconstructing details and delivering visually pleasant outputs. However, the overpowering generative ability can sometimes hallucinate and thus change the image content despite gaining high visual quality. This type of high-level change can be easily identified by humans yet not well-studied in existing low-level image quality metrics. In this paper, we establish the importance of measuring high-level fidelity for SR models as a complementary criterion to reveal the reliability of generative SR models. We construct the first annotated dataset with fidelity scores from different SR models, and evaluate how state-of-the-art (SOTA) SR models actually perform in preserving high-level fidelity. Based on the dataset, we then analyze how existing image quality metrics correlate with fidelity measurement, and further show that this high-level task can be better addressed by foundation models. Finally, by fine-tuning SR models based on our fidelity feedback, we show that both semantic fidelity and perceptual quality can be improved, demonstrating the potential value of our proposed criteria, both in model evaluation and optimization. We will release the dataset, code, and models upon acceptance.
Abstract (translated)
最近的图像超分辨率（SR）模型在重建细节和提供视觉上令人愉悦的结果方面取得了显著效果。然而，强大的生成能力有时会导致幻觉现象，并因此改变图像内容，尽管这些变化可能使视觉质量提高。这种高层次的变化虽然容易被人类识别，但现有的低层次图像质量度量却未能充分研究。本文确立了评估SR模型的高层次保真度的重要性，作为衡量生成型超分辨率模型可靠性的补充标准。我们构建了一个带有来自不同SR模型的保真度评分的数据集，并评估最先进的（SOTA）SR模型在保持高层次保真度方面的实际表现。基于该数据集，我们分析现有图像质量指标与保真度测量之间的相关性，并进一步表明基础模型可以更好地解决这一高级任务。最后，通过根据我们的保真度反馈微调SR模型，我们展示了语义保真度和感知质量都可以得到改善，证明了所提出的评价标准在模型评估和优化中的潜在价值。我们将接受后发布该数据集、代码及模型。
URL
https://arxiv.org/abs/2512.07037
