Diffusion-Enhancement-for-CR

🔥 Congradualation to my collaborators and myself! This paper has been accepted by IEEE Transactions on Geoscience and Remote Sensing! It's a new start!

The code for Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery, which is based on ADM.

Several conventional CR models could refer to https://github.com/littlebeen/Cloud-removal-model-collection!

Usage

Train

1. Pure diffusion. respace.py:gaussian_diffusion; unet.py: UnetModel
2. Locked diffusion + Trained WA :gaussian_diffusion_enhance; unet.py: UnetModel256; locked in train_util.py line74
3. ALL-in change train_util.py line74

python super_res_train.py

Test

1. Put the pre-train model into 'pre_train'

python super_res_sample.py

Weight

Our pre-train models on RICE2 with mn and mdsa are uploaded. https://pan.baidu.com/s/1SvnPL7HRKqSK0zDixYpHow code：bean

CUHK-CR

A novel multispectral Cloud Removal dataset

Download link: https://pan.baidu.com/s/1z2SgORYz5_t94kya8CeqiQ code:bean

-CUHK-CR1 (The RGB images for thin dataset CUHK-CR1)

-CUHK-CR2 (The RGB images for think dataset CUHK-CR2)

-nir (the near-infrared images for CUHK-CR1 and CUHK-CR2)

If you need image with 4 bands (RGB + nir), you could load the images in the RGB dataset and the nir dataset and combine the 4 channels together.

Cite

If this project is useful to you, please cite this paper :)

@article{sui2024diffusion,
  author={Sui, Jialu and Ma, Yiyang and Yang, Wenhan and Zhang, Xiaokang and Pun, Man-On and Liu, Jiaying},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery}, 
  year={2024},
  volume={62},
  pages={1-14},
  doi={10.1109/TGRS.2024.3411671}}

If you have any question, be free to contact with me!