Towards Automated Chinese Ancient Character Restoration: A Diffusion-based Method with a New Dataset
Here is the Python implementation of the paper "Towards Automated Chinese Ancient Character Restoration: A Diffusion-Based Method with a New Dataset".
The paper is accepted by AAAI24 and is available at: link
pip install -r requirements.txt
You just need to run the following code.
python train.py --epochs 2000 --time_steps 50 --input_dir None --output_dir ./output --localmask_dir ./mask ……After running, it will generate the model results in the folder ./output and the local mask results in the folder ./mask
To evaluate our results, you only need to run the following command.
the cmd to run evaluate
Repaired generated results will be placed in the folder below, and the evaluation metrics will be displayed in the command line (or in a file).
This is the experimental result
| Method | MAE ↓ | PSNR ↑ | SSIM ↑ | FID ↓ | LPIPS ↓ |
|---|---|---|---|---|---|
| DNCNN [zhang2017beyond] | 0.0873 | 21.04 | 0.9065 | 75.12 | 0.3925 |
| Cycle-Dehaze [engin2018cycle] | 0.1025 | 16.97 | 0.8862 | 92.19 | 0.4215 |
| VDN [guo2019toward] | 0.0619 | 21.46 | 0.9457 | 64.65 | 0.3078 |
| CIDG [zhang2020novel] | 0.0567 | 21.88 | 0.9271 | 49.96 | 0.2623 |
| SCCGAN [liu2021sccgan] | 0.0324 | 17.72 | 0.8976 | 36.59 | 0.1914 |
| SGGAN [li2021generative] | 0.0308 | 19.92 | 0.9673 | 33.24 | 0.0842 |
| IPT [chen2021pre] | 0.0169 | 23.73 | 0.9727 | 22.68 | 0.0777 |
| SwinIR [liang2021swinir] | 0.0195 | 24.08 | 0.9983 | 18.53 | 0.0483 |
| CharFormer [shi2022charformer] | 0.0226 | 24.38 | 0.9886 | 15.44 | 0.0557 |
| DiffACR(Ours) | 0.0187 | 22.25 | 0.9988 | 12.87 | 0.0494 |
If our code has been helpful to you, please don't forget to cite us.
Li H, Du C, Jiang Z, et al. Towards Automated Chinese Ancient Character Restoration: A Diffusion-Based Method with a New Dataset[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(4): 3073-3081.
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