RealRestorer：基於大規模圖像編輯模型的通用真實世界圖像修復研究

摘要

在真實世界退化條件下的影像復原技術，對於自動駕駛和物件檢測等下游任務至關重要。然而，現有復原模型往往受制於訓練資料的規模與分佈侷限，導致在真實場景中的泛化能力不足。近期，大規模影像編輯模型在復原任務中展現出強大的泛化能力，特別是像Nano Banana Pro這類閉源模型，能在保持一致性的前提下實現影像復原。但要使這類通用大模型達到此等性能，需要耗費大量資料與計算成本。為解決此問題，我們構建了涵蓋九種常見真實退化類型的大規模資料集，並訓練出頂尖開源模型以縮小與閉源方案的差距。此外，我們提出RealIR-Bench基準測試，包含464張真實退化影像及專注於退化消除與一致性保持的定制化評估指標。大量實驗表明，我們的模型在開源方法中位列第一，達到了最先進的性能表現。

English

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.

RealRestorer：基於大規模圖像編輯模型的通用真實世界圖像修復研究

RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models

摘要

Support