RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models
This work addresses the need for generalizable image restoration for applications like autonomous driving and object detection, but it is incremental as it builds on existing large-scale models.
The paper tackles the problem of poor generalization in real-world image restoration by constructing a large-scale dataset covering nine degradation types and training an open-source model, which ranks first among open-source methods with state-of-the-art performance.
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.