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.

翻译：真实退化条件下的图像复原对于自动驾驶、目标检测等下游任务至关重要。然而，现有复原模型常受限于训练数据的规模与分布，导致对真实场景的泛化能力不足。近期，大规模图像编辑模型在复原任务中展现出强大的泛化能力，尤以Nano Banana Pro等闭源模型为典型代表，其能在保持图像一致性的同时实现有效复原。然而，此类大型通用模型的性能提升需要大量数据与计算资源。为攻克这一难题，我们构建了一个覆盖九类常见真实退化类型的大规模数据集，并训练了当前最先进的开源模型以缩小与闭源方案的差距。此外，我们提出RealIR-Bench基准，该基准包含464张真实退化图像及侧重退化去除与一致性保持的定制化评估指标。大量实验表明，本模型在开源方法中排名首位，达到了业界领先水平。