Restore Text First, Enhance Image Later: Two-Stage Scene Text Image Super-Resolution with Glyph Structure Guidance
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915634348032000 |
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| author | Luo, Minxing Fan, Linlong Qiushi, Wang Wu, Ge Luo, Yiyan Yu, Yuhang Chen, Jinwei Wang, Yaxing Fan, Qingnan Yang, Jian |
| author_facet | Luo, Minxing Fan, Linlong Qiushi, Wang Wu, Ge Luo, Yiyan Yu, Yuhang Chen, Jinwei Wang, Yaxing Fan, Qingnan Yang, Jian |
| contents | Current image super-resolution methods show strong performance on natural images but distort text, creating a fundamental trade-off between image quality and textual readability. To address this, we introduce TIGER (Text-Image Guided supEr-Resolution), a novel two-stage framework that breaks this trade-off through a "text-first, image-later" paradigm. TIGER explicitly decouples glyph restoration from image enhancement: it first reconstructs precise text structures and uses them to guide full-image super-resolution. This ensures high fidelity and readability. To support comprehensive training and evaluation, we present the UZ-ST (UltraZoom-Scene Text) dataset, the first Chinese scene text dataset with extreme zoom. Extensive experiments show TIGER achieves state-of-the-art performance, enhancing readability and image quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Restore Text First, Enhance Image Later: Two-Stage Scene Text Image Super-Resolution with Glyph Structure Guidance Luo, Minxing Fan, Linlong Qiushi, Wang Wu, Ge Luo, Yiyan Yu, Yuhang Chen, Jinwei Wang, Yaxing Fan, Qingnan Yang, Jian Computer Vision and Pattern Recognition Current image super-resolution methods show strong performance on natural images but distort text, creating a fundamental trade-off between image quality and textual readability. To address this, we introduce TIGER (Text-Image Guided supEr-Resolution), a novel two-stage framework that breaks this trade-off through a "text-first, image-later" paradigm. TIGER explicitly decouples glyph restoration from image enhancement: it first reconstructs precise text structures and uses them to guide full-image super-resolution. This ensures high fidelity and readability. To support comprehensive training and evaluation, we present the UZ-ST (UltraZoom-Scene Text) dataset, the first Chinese scene text dataset with extreme zoom. Extensive experiments show TIGER achieves state-of-the-art performance, enhancing readability and image quality. |
| title | Restore Text First, Enhance Image Later: Two-Stage Scene Text Image Super-Resolution with Glyph Structure Guidance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.21590 |