This repository is the official PyTorch implementation of the TGRS 2024 paper DiffCR.
To install dependencies:
pip install -r requirements.txtTo download datasets:
-
Sen2_MTC_Old: multipleImage.tar.gz
-
Sen2_MTC_New: CTGAN.zip
-
SEN12MS-CR: https://mediatum.ub.tum.de/1554803
To train the models in the paper, run these commands:
python run.py -p train -c config/ours_sigmoid.json
To test the pre-trained models in the paper, run these commands:
python run.py -p test -c config/ours_sigmoid.jsonTo evaluate my models on two datasets, run:
python evaluation/eval.py -s [ground-truth image path] -d [predicted-sample image path]You can download pretrained models here:
- DiffCR trained on Sen2_MTC_Old dataset: 🤗 HuggingFace
- DiffCR trained on Sen2_MTC_New dataset: 🤗 HuggingFace
- DiffCR trained on SEN12MS-CR dataset (based on the official open-source implementation of UnCRtainTS): 🤗 HuggingFace
The visualization results of 12 methods (including DiffCR) on the test sets of Sen2_MTC_Old and Sen2_MTC_New datasets, along with evaluation code for direct comparison by researchers, are available at: 🤗 HuggingFace Visualization
├── paper-report.png ← reference metrics table from the paper
│
├── data/
│ ├── Sen2_MTC_New/
│ │ ├── GT/ ← 687 cloud-free ground-truth images ({id}.png)
│ │ └── inputs/ ← 687 × 3 cloudy input images
│ │ ({id}_A1.png {id}_A2.png {id}_A3.png)
│ └── Sen2_MTC_Old/
│ ├── GT/ ← 313 ground-truth images
│ └── inputs/ ← 313 × 3 cloudy inputs
│
├── results/
│ ├── Sen2_MTC_New/
│ │ ├── ae/ ← prediction images for each method ({id}.png)
│ │ ├── crtsnet/
│ │ ├── ctgan/
│ │ ├── ddpmcr/
│ │ ├── diffcr/ ← DiffCR [Ours]
│ │ ├── dsen2cr/
│ │ ├── mcgan/
│ │ ├── pix2pix/
│ │ ├── pmaa/
│ │ ├── stgan/
│ │ ├── stnet/
│ │ └── uncrtaints/
│ └── Sen2_MTC_Old/
│ └── (same 12 methods)
│
└── eval/
├── metrics.py ← PSNR / SSIM / FID / LPIPS evaluation
├── plot.py ← comparison figure generation
└── requirements.txt ← Python dependencies
If you use our code or models in your research, please cite with:
@ARTICLE{diffcr,
author={Zou, Xuechao and Li, Kai and Xing, Junliang and Zhang, Yu and Wang, Shiying and Jin, Lei and Tao, Pin},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal From Optical Satellite Images},
year={2024},
volume={62},
number={},
pages={1-14},
}
Janspiry/Palette-Image-to-Image-Diffusion-Models