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

2026-03-26

AI Summary

Existing image restoration models struggle to generalize to real-world complex degradation scenarios due to limitations in the scale and distribution of training data. To address this, this work presents the first large-scale training dataset encompassing nine categories of real-world degradations and leverages a large-scale image editing model architecture trained with explicit modeling of real degradations alongside strategies to preserve content consistency. Furthermore, we introduce RealIR-Bench, a new evaluation benchmark that jointly assesses degradation removal efficacy and content fidelity. Experimental results demonstrate that the proposed method significantly outperforms existing open-source models on RealIR-Bench, achieving state-of-the-art performance among open approaches and substantially narrowing the gap with closed-source counterparts.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• image restoration
• real-world degradations
• generalization
• large-scale models
• consistency preservation

Innovation

• real-world image restoration
• large-scale dataset
• open-source model
• generalization
• RealIR-Bench