---
title: 'GGT-100K: Real-World Restoration Dataset'
url: https://www.emergentmind.com/topics/ggt-100k
type: topic
---

# GGT-100K: Real-World Restoration Dataset

GGT-100K is a large-scale real-world image restoration dataset in which the high-quality supervision signal is not captured through a paired acquisition setup and not synthesized by a hand-designed degradation model, but generated from real low-quality images by a multimodal foundation model. Introduced together with the concept of **Generative Ground Truth (GGT)**, the dataset contains **103,707 training pairs** and a **500-pair test set**, all at **1024 × 1024**, and is designed to address the central bottleneck in real-world image restoration: the scarcity of realistic, diverse, and scalable paired training data for generalization beyond synthetic benchmarks [2605.31039].

## 1. Conceptual basis and problem setting

**Generative Ground Truth (GGT)** denotes a high-quality restoration target generated by a multimodal foundation model from a real degraded image. In this formulation, a real **low-quality (LQ)** image is taken from the wild, an MFM is prompted to produce an **idealized high-quality (HQ)** counterpart, and the generated HQ image is then used as supervision for restoration training [2605.31039].

The motivation is a two-sided data limitation. **Synthetic paired datasets** are scalable because they start from a clean HQ image and degrade it with a model, but they suffer from unrealistic degradations and a domain gap. **Manually captured paired datasets** provide realistic supervision, but they are expensive, difficult to align, hard to scale, and often infeasible for degradations such as rain, haze, motion blur, low light, or mixed distortions. GGT is positioned as a practical middle ground: more realistic than synthetic degradation pipelines and far more scalable than physically captured pairing.

This framing is especially relevant for real-world IR because the target domain is intrinsically open-ended. The six categories used in GGT-100K—**General Mixed**, **Low-Light**, **Haze**, **Rain**, **Snow**, and **Old Photo**—are explicitly not restricted to isolated single-degradation settings. A rain image, for example, may also contain blur, compression artifacts, and noise. This suggests that GGT-100K is structured around restoration under entangled real-world degradations rather than narrow task-factorized corruption models.

## 2. Data collection, degradation scope, and dataset composition

The source images for GGT-100K are collected from three channels: **existing datasets**, **internet sources**, and **self-captured images** [2605.31039]. The internet component is gathered via web crawling from CC0-licensed sources including **Unsplash**, **Pexels**, **Pixabay**, and **Flickr**. The self-captured portion uses different cameras and phones under blur, noise, low-light, and related real-world conditions. All images are normalized to **1024 × 1024**.

The final training set contains **103,707** paired samples, partitioned as follows:

| Category | Training pairs |
|---|---:|
| General Mixed | 66,058 |
| Low-Light | 9,786 |
| Haze | 7,822 |
| Rain | 7,177 |
| Snow | 6,759 |
| Old Photo | 6,105 |

Within **General Mixed**, the appendix reports **37,463 from internet images**, **19,438 from public datasets**, and **9,157 from self-captures**. Additional category-specific provenance is also specified: **Rain** uses NTURain, OpenReal80K, Flickr, Unsplash, and Pexels; **Haze** uses ACDC and RTTS; **Snow** uses Unsplash and Snow100K-Real; **Low-light** uses DarkFace, ExDark, and self-captures; **Old Photo** uses Flickr.

The raw collection process is substantially larger than the final released dataset. The appendix states that the authors screened **more than 1,100K image patches** and retained about **120K low-quality images** before the final MFM generation and quality-control stages. This indicates that dataset construction is not simply a one-pass generation procedure; it is a filtered pipeline in which raw diversity is intentionally narrowed into a reliability-controlled paired corpus.

## 3. Multimodal foundation model evaluation and model selection

Before constructing the dataset, the work performs a systematic evaluation of **nine multimodal foundation models** under multiple prompting strategies [2605.31039]. The nine MFMs are:

- **FireRed-1.1**
- **Qwen-Image-Edit-2511**
- **FLUX.2-dev**
- **Kling-Image-O1**
- **Seedream-5.0**
- **GPT-Image-1.5**
- **GPT-Image-2**
- **Nano-Banana-Pro**
- **Nano-Banana-2**

These include **3 open-source** and **6 closed-source** models. The evaluation does not treat model choice and prompting as separable. Instead, the paper emphasizes prompt sensitivity and compares three prompt regimes: **fixed prompts**, **fixed prompt with no-change constraint (Fix-NC)**, and **adaptive prompts** generated by a VLM such as **GPT-5.4-Pro** or **Gemini-3.1-Pro**.

Selection uses four complementary aspects: **fidelity preservation**, **perceptual quality**, **VLM-based restoration success**, and **human preference**. Fidelity is measured on **100 DIV2K validation images** degraded with the **Real-ESRGAN pipeline**, using **PSNR**, **SSIM**, **LPIPS**, and **DISTS**; low-light enhancement and dehazing are excluded from this fidelity evaluation because acceptable outputs may differ in global illumination. Perceptual evaluation uses **200 real-world images**—20 each for rain, haze, snow, low-light, old photo, and 100 mixed-degradation images—with **NIQE**, **MUSIQ**, **MANIQA**, **TOPIQ**, and **AFINE-NR**. The study also introduces **VLM-R**, a success rate defined as

$$
\mathrm{VLM\mbox{-}R}=\frac{1}{N}\sum_{i=1}^{N}\mathbb{I}(d_i=Accept)\times 100\%
$$

where \(N\) is the number of evaluated samples and \(d_i\) is the final VLM decision for sample \(i\).

For score aggregation, the paper defines a normalized overall score over fidelity, perceptual quality, and VLM judgment. The intended formula is

$$
\tilde{m}_{i,j}=\frac{m_{i,j}-m_j^{\min}{m_j^{\max}-m_j^{\min},\qquad s_i^{a}=\frac{1}{|\mathcal{M}_a|}\sum_{j\in\mathcal{M}_a}\tilde{m}_{i,j},\qquad \mathrm{Avg.}_i=\frac{1}{3}\left(s_i^{\mathrm{fid}+s_i^{\mathrm{per}+s_i^{\mathrm{vlm}\right)
$$

with \(a \in \{\mathrm{fid}, \mathrm{per}, \mathrm{vlm}\}\) and \(\mathcal{M}_a\) the metric set for aspect \(a\).

The selected configuration is **Nano-Banana-2 + Gemini-based adaptive prompting**, which achieves **best overall Avg. score: 0.84** and **highest human preference: 32.5%**. The comparison with GPT-generated prompts is operationally important: **Gemini-based prompting** is chosen because it performs better quantitatively, while GPT-based prompts sometimes blur or anonymize facial regions. The broader implication is that GGT-100K depends not only on MFM capability but also on prompt generation as a control variable in data synthesis.

## 4. GGT synthesis pipeline and multi-stage quality control

The GGT-100K construction pipeline has three stages: **collect real-world degraded images**, **evaluate MFMs and prompting strategies**, and **generate HQ targets and apply multi-stage quality control** [2605.31039]. The last stage is the core of the dataset methodology.

**Stage 1: metric-based filtering** compares no-reference perceptual scores between the original LQ image and the generated HQ image. If the generated output shows little improvement or worse quality, the sample is rejected. The paper describes this as a coarse automatic filter, especially useful for **General Mixed** images.

**Stage 2: VLM-assisted refinement** evaluates five criteria: **restoration quality**, **object consistency**, **geometry alignment**, **content reasonableness**, and **color consistency**. If a sample is judged unsatisfactory, it is not immediately discarded. Instead, the VLM feedback is used to modify the prompt, the HQ target is regenerated, and the result is re-evaluated. In this design, the VLM functions both as evaluator and feedback controller.

**Stage 3: manual verification** checks the final retained set for strong restoration quality, high fidelity, and absence of obvious hallucinations.

The appendix specifies an **iteration rule** of **up to three generation attempts per sample**: accept if the VLM approves; otherwise refine the prompt and regenerate; if still rejected, perform one more generation attempt with the same prompt. This operational detail is central to the dataset’s claim of reliability. The paper explicitly argues that raw MFM outputs are not safe to use directly; multi-stage filtering and VLM feedback are required to convert generative outputs into restoration supervision.

A plausible implication is that the contribution is as much about data curation policy as about generative synthesis. The dataset is not formed by treating MFM outputs as ground truth by default; it is formed by treating them as candidate ground truth subject to iterative acceptance control.

## 5. Benchmark protocol and empirical effects on restoration models

The work establishes a **500-pair test set** curated by multiple researchers and manually reviewed for high fidelity, avoidance of obvious hallucinations, and suitability for assessing restoration quality and content consistency [2605.31039]. Evaluation is reported both on the full **GGT-100K test set** and on category-specific subsets: **General Mixed**, **Rain**, **Haze**, **Snow**, **Low-Light**, and **Old Photo**. The paper also evaluates on public **RealLQ** datasets without ground truth using no-reference metrics and **VLM-R**.

The central empirical claim is that GGT-100K improves the real-world generalization of a wide range of IR models. The evaluated families include **CNN-based** methods (**MPRNet**, **NAFNet**), **Transformer-based** methods (**SwinIR**, **X-Restormer**), **all-in-one / blind restoration** methods (**PromptIR**, **MoCE-IR**, **DA-CLIP**, **FoundIR**), and **generative restoration** methods (**FLUX-Controlnet**, **Qwen-Image-Edit**).

On the full test set, adding GGT-100K improves **PSNR**, **SSIM**, **LPIPS**, **DISTS**, **MUSIQ**, **AFINE-NR**, and **VLM-R**. Representative examples include:

- **MPRNet**: **24.79 → 27.30** PSNR, **0.7637 → 0.8189** SSIM, **22.2% → 33.2%** VLM-R  
- **NAFNet**: **25.13 → 28.25** PSNR, **0.7708 → 0.8349** SSIM, **27.6% → 53.8%** VLM-R  
- **SwinIR**: **23.92 → 27.08** PSNR, **18.6% → 37.6%** VLM-R  
- **X-Restormer**: **24.69 → 28.23** PSNR, **30.4% → 54.6%** VLM-R  
- **PromptIR**: **24.57 → 28.18** PSNR, **24.8% → 49.6%** VLM-R  
- **MoCE-IR**: **24.86 → 28.21** PSNR, **25.4% → 55.2%** VLM-R  
- **DA-CLIP**: **25.88 → 26.69** PSNR, **31.6% → 51.0%** VLM-R  

The improvement pattern is differentiated by model class. The paper notes that **fidelity-oriented models** gain large PSNR improvements, whereas **generative models** benefit especially strongly in perceptual metrics and overall realism. For **FLUX-Controlnet**, the changes are **22.45 → 23.14** PSNR, **48.55 → 63.09** MUSIQ, **0.5157 → 0.5854** MANIQA, **0.3798 → 0.5013** TOPIQ, and **25.4% → 63.4%** VLM-R. For **Qwen-Image-Edit**, the reported changes are **25.86 → 26.18** PSNR, **0.2813 → 0.2155** LPIPS, **0.1625 → 0.1183** DISTS, **51.42 → 62.55** MUSIQ, and **77.4% → 87.6%** VLM-R.

On six public RealLQ test sets without ground truth, the addition of GGT-100K also generally improves no-reference quality and VLM-R. Reported VLM-R gains include **MPRNet: 38.0% → 45.0%**, **SwinIR: 28.0% → 38.7%**, **PromptIR: 41.3% → 48.3%**, **DA-CLIP: 46.7% → 55.0%**, **FLUX-Controlnet: 47.7% → 59.0%**, and **Qwen-Image-Edit: 60.7% → 70.7%**. These results support the paper’s claim that the dataset improves generalization beyond the nominal training distribution.

## 6. Ablations, limitations, and significance for real-world IR

A central ablation isolates the effect of quality control by comparing **baseline**, **w/o-QC**, and **w/-QC** training settings [2605.31039]. The conclusion is that even unscreened GGT-like data can help, but **quality control makes supervision more reliable and stable**. The strongest example is **FLUX-Controlnet**: **baseline PSNR: 22.45**, **w/o-QC PSNR: 19.42**, **w/-QC PSNR: 23.14**. This establishes that unscreened generated supervision can actively harm fidelity, whereas the filtered dataset recovers and improves performance. For **Qwen-Image-Edit**, **w/o-QC** already improves perceptual metrics substantially, but **w/-QC** further improves **PSNR**, **LPIPS**, **DISTS**, **MUSIQ**, and **VLM-R**.

The appendix further shows that gains are not confined to a single degradation category; improvements appear across **general mixed**, **rain**, **haze**, **snow**, **low-light**, and **old photo**. An important nuance appears in category-specific tables for **Qwen-Image-Edit**: on some rain and snow subsets, GGT-100K may lower PSNR while still improving perceptual metrics. The paper interprets this as better generated detail and realism without exact pixel matching, reinforcing the view that restoration quality should not be judged by pixel accuracy alone.

The limitations are explicit. First, GGT is **not perfect ground truth**: even after QC, some samples may still contain subtle artifacts, hallucinated details, or minor inconsistencies. Second, degradation coverage is broad but incomplete because real-world degradations are open-ended. Third, the experiments are described as **conservative**, in that they mostly use standard architectures and training setups rather than algorithms specifically optimized for GGT supervision.

Within these constraints, the significance of GGT-100K lies in reframing restoration dataset construction. The paper’s main implications are that **MFMs can be practical restoration-data generators**, that restoration datasets can be scaled without relying only on real paired capture, that **generative restoration models benefit especially strongly**, that prompting strategy materially affects data quality, and that **quality control is essential**. In that sense, GGT-100K is not merely a larger benchmark; it is an explicit proposal for using multimodal generative models, adaptive prompting, and iterative verification as infrastructure for real-world IR data generation.

Source: https://www.emergentmind.com/topics/ggt-100k