TGSR: Text-Grounded Diffusion for Faithful Image Super-Resolution

Key points

• However, for textual content, these priors often produce text hallucinations: non-existent or distorted text, which alter scene semantics and compromise model reliability.1
• Therefore, we reformulate text SR as a selective generation problem: text is resolved only when reliable generation is expected, while other scene content is fully super-resolved.1
• We introduce TGSR (Text-Grounded Super-Resolution), a test-time framework that progressively identifies and suppresses likely hallucinated text regions during generation.1
• TGSR consistently improves text fidelity across multiple backbones, increasing recognition precision by up to 17.68 points without degrading perceptual quality, underscoring the value of selective control in generative SR.1

Sources (1)

• [1]TGSR: Text-Grounded Diffusion for Faithful Image Super-Resolution
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 08:00 PM

  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 compromise model reliability.

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