mlsanityData to 14 Oct 2026

G-Refine: A General Quality Refiner for Text-to-Image Generation

arXiv 2404.18343 ↗First posted 29 Apr 2024cs.MM cs.CVcited by 8

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Abstract (arXiv)

With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier to their widespread adoption. In terms of both perception and alignment, existing models cannot always guarantee high-quality results. To mitigate this limitation, we introduce G-Refine, a general image quality refiner designed to enhance low-quality images without compromising the integrity of high-quality ones. The model is composed of three interconnected modules: a perception quality indicator, an alignment quality indicator, and a general quality enhancement module. Based on the mechanisms of the Human Visual System (HVS) and syntax trees, the first two indicators can respectively identify the perception and alignment deficiencies, and the last module can apply targeted quality enhancement accordingly. Extensive experimentation reveals that when compared to alternative optimization methods, AIGIs after G-Refine outperform in 10+ quality metrics across 4 databases. This improvement significantly contributes to the practical application of contemporary T2I models, paving the way for their broader adoption. The code will be released on https://github.com/Q-Future/Q-Refine.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 37 of its 66 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

37 of 66 references.

References · 37

  1. 2023Segment Anythingcited by 8,234
  2. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
  3. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
  4. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 991
  5. 2023Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generationcited by 977
  6. 2021DenseCLIP: Language-Guided Dense Prediction with Context-Aware Promptingcited by 532
  7. 2023Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levelscited by 503
  8. 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 489
  9. 2017NIMA: Neural Image Assessmentcited by 389
  10. 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
  11. 2023Human Preference Score: Better Aligning Text-to-Image Models with Human Preferencecited by 287
  12. 2023Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspectivecited by 280
25 more
  1. 2023DiffBIR: Towards Blind Image Restoration with Generative Diffusion Priorcited by 257
  2. 2023Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylizationcited by 254
  3. 2023Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Visioncited by 249
  4. 2019From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Qualitycited by 223
  5. 2023FreeU: Free Lunch in Diffusion U-Netcited by 196
  6. 2023Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Modelscited by 152
  7. 2020Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wildcited by 134
  8. 2024Towards Open-ended Visual Quality Comparisoncited by 85
  9. 2020Unsupervised Real-world Image Super Resolution via Domain-distance Aware Trainingcited by 83
  10. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 73
  11. 2024Perceptual Video Quality Assessment: A Surveycited by 43
  12. 2024MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Modelcited by 30
  13. 2023Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluationcited by 27
  14. 2024TextCraftor: Your Text Encoder Can be Image Quality Controllercited by 27
  15. 2020Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessmentcited by 24
  16. 2023Exploring the Naturalness of AI-Generated Imagescited by 22
  17. 2023Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Modelscited by 22
  18. 2023GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessmentcited by 21
  19. 2024Q-Refine: A Perceptual Quality Refiner for AI-Generated Imagecited by 21
  20. 2024NTIRE 2024 Quality Assessment of AI-Generated Content Challengecited by 20
  21. 2024AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Imagescited by 16
  22. 2021Troubleshooting Blind Image Quality Models in the Wildcited by 14
  23. 2022Measuring Perceptual Color Differences of Smartphone Photographscited by 8
  24. 2024THQA: A Perceptual Quality Assessment Database for Talking Headscited by 8
  25. 2023Learning a Deep Color Difference Metric for Photographic Imagescited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 2.

8 papers.

  1. 2024A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trendscited by 60
  2. 2024R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?cited by 30
  3. 2025ImAgent: A Unified Multimodal Agent Framework for Test-Time Scalable Image Generationcited by 17
  4. 2024VQA2: Visual Question Answering for Video Quality Assessmentcited by 15
  5. 2025Image Quality Assessment: From Human to Machine Preferencecited by 12
  6. 2024CCDM: Continuous Conditional Diffusion Models for Image Generationcited by 8
  7. 2026rPPG-VQA: A Video Quality Assessment Framework for Unsupervised rPPG Trainingcited by 1
  8. 2026From Open Loop to Closed Loop: A Test-Time Iterative Optimization Framework for Reference-Consistent Image Generationcited by 0