DeS3_Deshadow (AAAI'2024)

Introduction

This is an implementation of DeS3: Attention-driven Self and Soft Shadow Removal using ViT Similarity and Color Convergence

1. Datasets

1. SRD Train|BaiduPan, Test. Shadow Masks

2. AISTD|ISTD+ [link]

3. LRSS: Soft Shadow Dataset [link]

4. USR: Unpaired Shadow Removal Dataset [link]

5. UCF, UIUC: Self Shadow [link]

2. SRD Dataset Results:

Dropbox | BaiduPan code:blk7

SRD Dataset Evaluation

1. set the paths of the shadow removal result and the dataset in evaluation/demo_SRD_RMSE.m and then run it.

demo_SRD_RMSE.m

Get the RMSE from Table 1 in the main paper on the SRD (size: 256x256):

Method	Training	Shadow	Non-Shadow	ALL
DeS3	Paired	5.88	2.83	3.72

2. set the paths of the shadow removal result and the dataset in evaluation/evaluate_SRD_PSNR_SSIM.m and then run it.

evaluate_SRD_PSNR_SSIM.m

Get the PSNR & SSIM from Table 1 in the main paper on the SRD (size: 256x256):

		PSNR	PSNR	PSNR	SSIM	SSIM	SSIM
Method	Training	Shadow	Non-Shadow	ALL	Shadow	Non-Shadow	ALL
DeS3	Paired	37.45	38.12	34.11	0.984	0.988	0.968

Acknowledgments

Code is implemented based WeatherDiffusion, we would like to thank them.

License

The code and models in this repository are licensed under the MIT License for academic and other non-commercial uses.
For commercial use of the code and models, separate commercial licensing is available. Please contact:

• Jonathan Tan (jonathan_tano@nus.edu.sg)

Citations

If this work is useful for your research, please cite our paper.

@inproceedings{jin2024des3,
  title={DeS3: Adaptive Attention-Driven Self and Soft Shadow Removal Using ViT Similarity},
  author={Jin, Yeying and Ye, Wei and Yang, Wenhan and Yuan, Yuan and Tan, Robby T},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={3},
  pages={2634--2642},
  year={2024}
}