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简体中文 | English | Paper

TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution

Here is the official reproduction repository of the paper TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution. TextDiff is a scene text super-resolution optimization model (see paper for details).

Network Structure

User Guide

Environment configuration

Deep Learning Environment

  • python >= 3.7
  • pytorch >= 1.7.0
  • torchvision >= 0.8.0
  • lmdb >= 0.98
  • pillow >= 7.1.2
  • numpy
  • six
  • tqdm
  • python-opencv
  • easydict
  • yaml

Dataset

Related weight files

To-do lists

  • Add training code
  • Add inference code
  • Use DPM_solver to reduce inference step size

Renderings

Gratitude

  • If you think TextDiff is helpful to you, please give it a star, thank you!
  • If you have any questions, please raise an issue and I will reply as soon as possible.
  • If you are willing to use TextDiff as a baseline for your project, you are welcome to cite our paper.

References

  • [1] Scene text telescope: Text-focused scene image super-resolution
  • [2] Activating more pixels in image super-resolution transformer.
  • [3] Srdiff: Single image super-resolution with diffusion probabilistic models.
  • [4] DocDiff: Document Enhancement via Residual Diffusion Models
  • [5] Improving Scene Text Image Super-Resolution via Dual Prior Modulation Network

📖 Citation

If you use (part of) my code or find my work helpful, please consider citing

@article{liu2023textdiff,
  title={TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution},
  author={Liu, Baolin and Yang, Zongyuan and Wang, Pengfei and Zhou, Junjie and Liu, Ziqi and Song, Ziyi and Liu, Yan and Xiong, Yongping},
  journal={arXiv preprint arXiv:2308.06743},
  year={2023}
}

Acknowledgement

This code is developed relying on DocDiff and TATT. Thanks for these great projects. Among them, DocDiff is the main research content of my classmate, and I participated in part of the research.