Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918058554032128 |
|---|---|
| author | Pan, Chenglu Xu, Xiaogang Ding, Ganggui Zhang, Yunke Li, Wenbo Xu, Jiarong Wu, Qingbiao |
| author_facet | Pan, Chenglu Xu, Xiaogang Ding, Ganggui Zhang, Yunke Li, Wenbo Xu, Jiarong Wu, Qingbiao |
| contents | Restoring low-resolution text images presents a significant challenge, as it requires maintaining both the fidelity and stylistic realism of the text in restored images. Existing text image restoration methods often fall short in hard situations, as the traditional super-resolution models cannot guarantee clarity, while diffusion-based methods fail to maintain fidelity. In this paper, we introduce a novel framework aimed at improving the generalization ability of diffusion models for text image super-resolution (SR), especially promoting fidelity. First, we propose a progressive data sampling strategy that incorporates diverse image types at different stages of training, stabilizing the convergence and improving the generalization. For the network architecture, we leverage a pre-trained SR prior to provide robust spatial reasoning capabilities, enhancing the model's ability to preserve textual information. Additionally, we employ a cross-attention mechanism to better integrate textual priors. To further reduce errors in textual priors, we utilize confidence scores to dynamically adjust the importance of textual features during training. Extensive experiments on real-world datasets demonstrate that our approach not only produces text images with more realistic visual appearances but also improves the accuracy of text structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios Pan, Chenglu Xu, Xiaogang Ding, Ganggui Zhang, Yunke Li, Wenbo Xu, Jiarong Wu, Qingbiao Computer Vision and Pattern Recognition Restoring low-resolution text images presents a significant challenge, as it requires maintaining both the fidelity and stylistic realism of the text in restored images. Existing text image restoration methods often fall short in hard situations, as the traditional super-resolution models cannot guarantee clarity, while diffusion-based methods fail to maintain fidelity. In this paper, we introduce a novel framework aimed at improving the generalization ability of diffusion models for text image super-resolution (SR), especially promoting fidelity. First, we propose a progressive data sampling strategy that incorporates diverse image types at different stages of training, stabilizing the convergence and improving the generalization. For the network architecture, we leverage a pre-trained SR prior to provide robust spatial reasoning capabilities, enhancing the model's ability to preserve textual information. Additionally, we employ a cross-attention mechanism to better integrate textual priors. To further reduce errors in textual priors, we utilize confidence scores to dynamically adjust the importance of textual features during training. Extensive experiments on real-world datasets demonstrate that our approach not only produces text images with more realistic visual appearances but also improves the accuracy of text structure. |
| title | Boosting Diffusion-Based Text Image Super-Resolution Model Towards Generalized Real-World Scenarios |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07232 |