Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration（C2F-DFT）
Updating!!!
Coarse Training Pipeline of Diffusion Transformer Model (DFT)
Fine Training Pipeline and Sampling Phase
Requirements
- CUDA 10.1 (or later)
- Python 3.9 (or later)
- Pytorch 1.8.1 (or later)
- Torchvision 0.19
- OpenCV 4.7.0
- tensorboard, skimage, scipy, lmdb, tqdm, yaml, einops, natsort
Training and Evaluation
Training and testing instructions and visualization results for Image Deraining, Image Deblurring, and Real Image Denoising are provied in the links below.
| Task | Training | Testing | C2F-DFT's Visual Results |
|---|---|---|---|
| Image Deraining | Link | Link | Download |
| Image Deblurring | Link | Link | Download |
| Real Image Denoising | Link | Link | Download |
Experimental Results
Image Deraining (click to expand)
Image Deblurring (click to expand)
Real Image Denoising (click to expand)
Contact
Acknowledgment: This code is based on the BasicSR toolbox and Restormer, WeatherDiffusion.