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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.04641 |
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| _version_ | 1866918046333927424 |
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| author | Hu, Qiming Fan, Linlong Luo, Yiyan Yu, Yuhang Guo, Xiaojie Fan, Qingnan |
| author_facet | Hu, Qiming Fan, Linlong Luo, Yiyan Yu, Yuhang Guo, Xiaojie Fan, Qingnan |
| contents | The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues, particularly distorting textual structures. In this paper, we introduce a novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images. Moreover, we propose a complete pipeline for synthesizing high-quality images with fine-grained full-image text masks, combining realistic foreground text regions with detailed background content. Extensive experiments demonstrate that our approach substantially enhances text legibility in super-resolved images, achieving state-of-the-art performance across multiple evaluation metrics and exhibiting strong generalization to real-world scenarios. Our code is available at \href{https://github.com/mingcv/TADiSR}{here}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04641 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders Hu, Qiming Fan, Linlong Luo, Yiyan Yu, Yuhang Guo, Xiaojie Fan, Qingnan Computer Vision and Pattern Recognition The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues, particularly distorting textual structures. In this paper, we introduce a novel diffusion-based SR framework, namely TADiSR, which integrates text-aware attention and joint segmentation decoders to recover not only natural details but also the structural fidelity of text regions in degraded real-world images. Moreover, we propose a complete pipeline for synthesizing high-quality images with fine-grained full-image text masks, combining realistic foreground text regions with detailed background content. Extensive experiments demonstrate that our approach substantially enhances text legibility in super-resolved images, achieving state-of-the-art performance across multiple evaluation metrics and exhibiting strong generalization to real-world scenarios. Our code is available at \href{https://github.com/mingcv/TADiSR}{here}. |
| title | Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.04641 |