Abstract
Recent advances in generative AI raise the question of whether general-purpose image editing models can serve as unified solutions for image restoration. In this work, we conduct a systematic evaluation of Nano Banana 2 for image restoration across diverse scenes and degradation types. Our results show that prompt design plays a critical role, where concise prompts with explicit fidelity constraints achieve the best trade-off between reconstruction accuracy and perceptual quality. Compared with state-of-the-art restoration models, Nano Banana 2 achieves superior performance in full-reference metrics while remaining competitive in perceptual quality, which is further supported by user studies. We also observe strong generalization in challenging scenarios, such as small faces, dense crowds, and severe degradations. However, the model remains sensitive to prompt formulation and may require iterative refinement for optimal results. Overall, our findings suggest that general-purpose generative models hold strong potential as unified image restoration solvers, while highlighting the importance of controllability and robustness. All test results are available on this https URL.
Abstract (translated)
生成式AI的最新进展引发了这样一个问题：通用图像编辑模型能否作为图像修复的统一解决方案。在本研究中，我们对Nano Banana 2在多样场景和退化类型下的图像修复能力进行了系统评估。结果表明，提示词设计起着关键作用——简洁且包含明确保真度约束的提示词能在重建精度与感知质量间取得最佳平衡。与最先进的修复模型相比，Nano Banana 2在全参考指标上表现更优，同时在感知质量方面保持竞争力，这一点也得到用户研究的佐证。我们还观察到该模型在挑战性场景（如小人脸、密集人群和严重退化）中展现出强大泛化能力。然而，模型仍对提示词表述敏感，可能需要通过迭代调整才能获得最优结果。总体而言，我们的发现表明通用生成模型作为统一图像修复解决方案具有巨大潜力，同时也凸显了可控性与鲁棒性的重要性。所有测试结果已公开于该链接。
URL
https://arxiv.org/abs/2604.03061
