RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models
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.
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Yufeng Yang
claimed_verified
2026-03-27T11:33:30.505Z
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Yufeng Yang
dericky286
user
dericky286
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Xianfang Zeng
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Zhangqi Jiang
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Fukun Yin
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Jianzhuang Liu
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Wei Cheng
claimed_verified
2026-03-27T11:33:32.840Z
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Wei Cheng
wchengad
user
wchengad
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jinghong lan
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Shiyu Liu
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Yuqi Peng
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Gang YU
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Shifeng Chen
RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models
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.
A large-scale dataset and open-source model are developed to improve image restoration performance and close the gap with closed-source alternatives, with a dedicated benchmark for real-world degradation evaluation.
image restoration real-world degradations large-scale dataset open-source model closed-source models RealIR-Bench degradation removal consistency preservation