Paper page - GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration

Source: https://huggingface.co/papers/2605.31039

Abstract

Generative multimodal foundation models are used to create high-quality training data for image restoration, improving model generalization across diverse real-world scenarios.

Real-worldimage restoration(IR) is bottlenecked by the scarcity of high-quality paired training data.Synthetic datasetsare abundant but often fail to modelreal-world degradations, while real-world paired datasets are expensive and difficult to capture. As a result, IR models trained on these datasets show limited generalization in real-world scenarios. In this work, we proposeGenerative Ground Truth(GGT) by usinggenerative multimodal foundation models(MFMs) to produce high-quality (HQ) targets from real-world low-quality (LQ) images. We first conduct a systematic evaluation of nine state-of-the-art MFMs, includingNano-Banana-2and GPT-Image-2, on images of various scenes and degradation types. The results demonstrate thatNano-Banana-2withVLM-based adaptive promptingshows the highest capability to synthesize perceptually realistic and content-faithful HQ targets, which can serve as the GGT for the LQ input. We then employNano-Banana-2to build a GGT synthesis pipeline, which involvesmulti-stage quality controlto ensure data reliability, and construct GGT-100K, anLQ-HQ paired datasetcomprising 103,707 training pairs and covering diverse scenes and complexreal-world degradations. A test set of 500 image pairs is also established. Extensive experiments show that GGT-100K consistently improves the real-world generalization of a wide range of IR models, with particularly strong benefits for finetuning generative models for IR tasks. Our results suggest that MFMs can serve as practical tools for restoration-oriented data generation, and GGT-100K is a useful resource to expand the generalization boundaries of real-world IR models.

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