RealRestorer：迈向通用的现实世界图像修复的大型图像编辑模型
RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models
AI总结：
本文提出RealRestorer，通过构建大规模数据集和训练先进开源模型，提升现实世界图像修复的泛化能力，并引入RealIR-Bench评估基准，实验表明其在开源方法中表现最佳。
摘要：
现实世界退化图像修复对于自动驾驶和目标检测等下游任务至关重要。然而，现有修复模型往往受限于训练数据的规模和分布，导致在现实场景中泛化能力差。最近，大规模图像编辑模型在修复任务中表现出强大的泛化能力，尤其是闭源模型如Nano Banana Pro，能够在修复图像的同时保持一致性。然而，要实现这种性能，使用这些大规模通用模型需要大量的数据和计算成本。为了解决这个问题，我们构建了一个涵盖九种常见现实世界退化类型的大型数据集，并训练了一个最先进的开源模型，以缩小与闭源替代品之间的差距。此外，我们引入了RealIR-Bench，其中包含464张现实世界退化的图像和定制的评估指标，重点在于退化去除和一致性保持。广泛实验表明，我们的模型在开源方法中排名第一，实现了最先进的性能。
英文摘要：
Image restoration under real-world degradations is critical for downstream tasks such as autonomous driving and object detection. However, existing restoration models are often limited by the scale and distribution of their training data, resulting in poor generalization to real-world scenarios. Recently, large-scale image editing models have shown strong generalization ability in restoration tasks, especially for closed-source models like Nano Banana Pro, which can restore images while preserving consistency. Nevertheless, achieving such performance with those large universal models requires substantial data and computational costs. To address this issue, we construct a large-scale dataset covering nine common real-world degradation types and train a state-of-the-art open-source model to narrow the gap with closed-source alternatives. Furthermore, we introduce RealIR-Bench, which contains 464 real-world degraded images and tailored evaluation metrics focusing on degradation removal and consistency preservation. Extensive experiments demonstrate our model ranks first among open-source methods, achieving state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
