TGSR: Text-Grounded Diffusion for Faithful Image Super-Resolution
Diffusion models have achieved remarkable success in image super-resolution (SR) by leveraging strong generative priors to enhance perceptual quality. However, for textual content, these priors often produce text hallucinations: non-existent or distorted text, which alter scene semantics and…
The article introduces TGSR, a novel framework for text-grounded image super-resolution that addresses text hallucinations in diffusion models by selectively controlling text generation during the super-resolution process.
What happened
The article presents TGSR, a test-time framework designed to enhance text fidelity in image super-resolution by identifying and suppressing hallucinated text regions during generation. The method leverages dedicated detectors trained on a large-scale dataset of diffusion-based SR outputs to selectively control text generation, ensuring that text is only resolved when reliable, while other scene content is fully super-resolved.
Why it matters
Text hallucinations in image super-resolution can significantly alter scene semantics and reduce model reliability, making it crucial for AI developers and researchers to address these issues. TGSR's selective control approach offers a practical solution to improve the accuracy and trustworthiness of generated images, particularly in applications where text fidelity is essential.
- Selective control in text generation is essential for improving text fidelity in image super-resolution.
- TGSR's framework effectively suppresses hallucinated text without compromising perceptual quality.
- The method's applicability across multiple backbones makes it a versatile solution for various SR models.
The bigger picture
TGSR's approach has the potential to enhance the reliability and accuracy of image super-resolution systems, particularly in scenarios involving text. By reducing text hallucinations, it improves the practical utility of these models in real-world applications where text clarity and correctness are critical.
TGSR presents a significant advancement in image super-resolution by addressing text hallucinations through selective generation control, offering improved text fidelity and reliability for AI-driven image processing tasks.
Technical details
- Diffusion models for image super-resolution
- Selective generation problem formulation
- Dedicated detectors for hallucinated text regions
- Large-scale dataset for training detectors
- Test-time framework for real-time text suppression
FAQ
What is TGSR?
TGSR (Text-Grounded Super-Resolution) is a framework designed to enhance text fidelity in image super-resolution by selectively controlling text generation to suppress hallucinations.
How does TGSR address text hallucinations?
TGSR uses dedicated detectors trained on a large-scale dataset of diffusion-based SR outputs to identify and suppress likely hallucinated text regions during generation.
What are the benefits of TGSR?
TGSR improves text recognition precision by up to 17.68 points without degrading perceptual quality, making it a reliable solution for text-focused image super-resolution tasks.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
Read the full story
Originally published by arXiv - cs.CV (CV) on Oct 14, 2026.
Comments
Sign in to join the discussion.