  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.
