- Please put datasets in the folder
Datasets/
. - Follow the instructions below to begin training our model.
bash train.sh
Run the script then you can find the generated experimental logs in the folder checkpoints
.
- Follow the instructions below to begin testing our model.
python test.py
Run the script then you can find the output visual results in the folder results/
.
Models | MSDT |
---|---|
Rain200L | Google Drive / Baidu Netdisk (8ajd) |
Rain200H | Google Drive / Baidu Netdisk (97lm) |
DID-Data | Google Drive / Baidu Netdisk (5g4p) |
DDN-Data | Google Drive / Baidu Netdisk (b0b5) |
SPA-Data | Google Drive / Baidu Netdisk (x0i5) |
See folder "evaluations"
-
for Rain200L/H and SPA-Data datasets: PSNR and SSIM results are computed by using this Matlab Code.
-
for DID-Data and DDN-Data datasets: PSNR and SSIM results are computed by using this Matlab Code.
Methods | MSDT |
---|---|
Rain200L | Baidu Netdisk (1xkc) |
Rain200H | Baidu Netdisk (yr3n) |
DID-Data | Baidu Netdisk (242e) |
DDN-Data | Baidu Netdisk (2pwk) |
SPA-Data | Baidu Netdisk (cag0) |
Thanks for their awesome works (DeepRFT and DRSformer).
Please consider citing our work as follows if it is helpful.
@inproceedings{chen2024rethinking,
title={Rethinking Multi-Scale Representations in Deep Deraining Transformer},
author={Chen, Hongming and Chen, Xiang and Lu, Jiyang and Li, Yufeng},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={2},
pages={1046--1053},
year={2024}
}