用结构先验提升中文文本图像超分辨率，还原细粒度笔画细节。
Enhanced Generative Structure Prior for Chinese Text Image Super-resolution
- 提出字符级结构先验，结合代码本与StyleGAN生成高精度笔画
- 在真实不规则布局下仍能恢复清晰笔画，优于传统方法
- 适合中文OCR、古籍数字化等需精细文本重建的场景
忠实的文本图像超分辨率（SR）极具挑战性，因每个汉字具有独特结构，且字体风格和排版多样。现有方法多聚焦英文文本，对复杂中文文本关注较少。本文提出一种高质量中文文本图像超分辨率框架，旨在恢复低分辨率（LR）中文字符的精确笔画。不同于依赖字符识别先验的方法，我们提出新型结构先验，在结构层面提供引导以提升视觉质量。该框架将结构先验嵌入StyleGAN模型中，利用其生成能力进行修复。为保持字符结构完整性并适应不同字体风格与布局，设计基于代码本的机制，限制StyleGAN的生成空间：代码本中的每个码代表特定字符的结构，而StyleGAN中的向量$w$控制字体、方向、位置等风格。通过代码本与风格向量的协同作用，生成与低分辨率字符在空间和结构上对齐的高分辨率结构先验。实验表明，该结构先验提供稳健的字符级指导，可在真实世界低分辨率中文文本（含不规则布局）中准确恢复清晰笔画。代码与预训练模型将开源于https://github.com/csxmli2016/MARCONetPlusPlus。
原文摘要 · Abstract (English)
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
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