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.

翻译：随着文本到图像（T2I）模型的演进，人工智能生成图像（AIGI）的质量缺陷已成为其广泛采用的主要障碍。现有模型在感知质量和文本对齐两方面均无法始终保证高质量输出。为解决此局限，我们提出G-Refine——一种通用图像质量优化器，旨在提升低质量图像的同时不损害高质量图像的完整性。该模型由三个相互关联的模块构成：感知质量指标、对齐质量指标和通用质量增强模块。基于人类视觉系统（HVS）机制与语法树结构，前两个指标可分别识别感知缺陷与对齐缺陷，最后一个模块则能据此进行针对性质量增强。大量实验表明，与替代优化方法相比，经G-Refine处理的AIGI在四个数据库的10余项质量指标上均表现更优。这一改进显著促进了当代T2I模型的实际应用，为其更广泛的推广奠定了基础。代码将发布于https://github.com/Q-Future/Q-Refine。