Core Concepts
The author proposes a diffusion-based approach for blind text image super-resolution, utilizing Image Diffusion Models (IDM) and Text Diffusion Models (TDM) to restore text images with high fidelity and style realness.
Abstract
The study focuses on restoring degraded low-resolution text images, especially Chinese characters, using diffusion models. The proposed method combines IDM and TDM to achieve realistic text structures and styles simultaneously. Extensive experiments demonstrate the effectiveness of the approach on synthetic and real-world datasets.
The content discusses the challenges in blind text image super-resolution, emphasizing the importance of maintaining text fidelity and style realness. Different methods are compared based on quantitative metrics such as PSNR, LPIPS, FID, ACC, and NED. Additionally, qualitative comparisons showcase the visual results of various approaches.
An ablation study is conducted to validate the effectiveness of initial text recognition (TR), TDM, and MoM components in improving text image restoration quality. The results highlight the significance of incorporating these components for better performance.
Overall, the study presents a novel approach using diffusion models for blind text image super-resolution, showcasing promising results in terms of both quantitative metrics and visual quality comparisons.
Stats
63,644 HR text images XHR remaining with annotations c.
8,089 LR-HR pairs in CTR-TSR-Test synthetic dataset.
1,531 LR-HR pairs in RealCE real-world dataset.
Quotes
"Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution."
"Our method can restore text images with more accurate text structures as well as more realistic appearances simultaneously."