Enhanced Generative Structure Prior for Chinese Text Image Super-resolution

• URL: http://arxiv.org/abs/2508.07537v1
• Date: Mon, 11 Aug 2025 01:34:45 GMT
• Title: Enhanced Generative Structure Prior for Chinese Text Image Super-resolution
• Authors: Xiaoming Li, Wangmeng Zuo, Chen Change Loy,
• Abstract summary: We introduce a text image framework designed to restore the precise strokes of low-resolution (LR) Chinese characters.<n>Our framework incorporates this structure prior within a StyleGAN model.<n>Our code and pre-trained models will be available at https://github.com/csi2016/MARCONetPlus.
• Score: 101.66745917380837
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Faithful text image super-resolution (SR) is challenging because each character has a unique structure and usually exhibits diverse font styles and layouts. While existing methods primarily focus on English text, less attention has been paid to more complex scripts like Chinese. In this paper, we introduce a high-quality text image SR framework designed to restore the precise strokes of low-resolution (LR) Chinese characters. Unlike methods that rely on character recognition priors to regularize the SR task, we propose a novel structure prior that offers structure-level guidance to enhance visual quality. Our framework incorporates this structure prior within a StyleGAN model, leveraging its generative capabilities for restoration. To maintain the integrity of character structures while accommodating various font styles and layouts, we implement a codebook-based mechanism that restricts the generative space of StyleGAN. Each code in the codebook represents the structure of a specific character, while the vector $w$ in StyleGAN controls the character's style, including typeface, orientation, and location. Through the collaborative interaction between the codebook and style, we generate a high-resolution structure prior that aligns with LR characters both spatially and structurally. Experiments demonstrate that this structure prior provides robust, character-specific guidance, enabling the accurate restoration of clear strokes in degraded characters, even for real-world LR Chinese text with irregular layouts. Our code and pre-trained models will be available at https://github.com/csxmli2016/MARCONetPlusPlus

Related papers

• TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering [76.53315206999231]
  TextPecker is a plug-and-play structural anomaly perceptive RL strategy.<n>It mitigates noisy reward signals and works with any textto-image generators.<n>It significantly yields average gains of 4% in structural fidelity and 8.7% in semantic alignment for Chinese text rendering.
  arXiv  Detail & Related papers  (2026-02-24T13:40:23Z)
• Zero-Shot Chinese Character Recognition with Hierarchical Multi-Granularity Image-Text Aligning [52.92837273570818]
  Chinese characters exhibit unique structures and compositional rules, allowing for the use of fine-grained semantic information in representation.<n>We propose a Hierarchical Multi-Granularity Image-Text Aligning (Hi-GITA) framework based on a contrastive paradigm.<n>Our proposed Hi-GITA outperforms existing zero-shot CCR methods.
  arXiv  Detail & Related papers  (2025-05-30T17:39:14Z)
• GlyphMastero: A Glyph Encoder for High-Fidelity Scene Text Editing [23.64662356622401]
  We present GlyphMastero, a specialized glyph encoder designed to guide the latent diffusion model for generating texts with stroke-level precision.<n>Our method achieves an 18.02% improvement in sentence accuracy over the state-of-the-art scene text editing baseline.
  arXiv  Detail & Related papers  (2025-05-08T03:11:58Z)
• Towards Visual Text Design Transfer Across Languages [49.78504488452978]
  We introduce a novel task of Multimodal Style Translation (MuST-Bench) MuST-Bench is a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems. In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions.
  arXiv  Detail & Related papers  (2024-10-24T15:15:01Z)
• VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization [52.870638830417]
  We propose a VQGAN-based framework (i.e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement. Specifically, we pre-train a VQGAN to encapsulate font token prior within a codebook. Subsequently, VQ-Font refines the synthesized glyphs with the codebook to eliminate the domain gap between synthesized and real-world strokes.
  arXiv  Detail & Related papers  (2023-08-27T06:32:20Z)
• A Benchmark for Chinese-English Scene Text Image Super-resolution [15.042152725255171]
  Scene Text Image Super-resolution (STISR) aims to recover high-resolution (HR) scene text images with visually pleasant and readable text content from low-resolution (LR) input. Most existing works focus on recovering English texts, which have relatively simple character structures. We propose a real-world Chinese-English benchmark dataset, namely Real-CE, for the task of STISR.
  arXiv  Detail & Related papers  (2023-08-07T02:57:48Z)
• Learning Generative Structure Prior for Blind Text Image Super-resolution [153.05759524358467]
  We present a novel prior that focuses more on the character structure. To restrict the generative space of StyleGAN, we store the discrete features for each character in a codebook. The proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character.
  arXiv  Detail & Related papers  (2023-03-26T13:54:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.